{"as_of":"2026-08-11T15:48:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:bc738054a0781a2f8f406fd022e0bf78328d31a429110ba2d5ff39cd530cdd4c","coverage":[{"denominator":56,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":56,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:33:56.021465Z","state":"measured"},{"denominator":57,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":57,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-11T06:34:44.6726+00:00","state":"measured"},{"denominator":1,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":1,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-05T10:33:50.835577Z","state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"pith","source_observed_at":"2026-08-05T10:33:56.639846Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"cited_work":{"arxiv_id":"2509.03975","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.03975","snapshot_observed_at":"2026-08-05T10:33:56.639846Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","venue":"cs.CV","work_id":"370dc768-157b-4c92-81a5-8a9cbc794f66","year":2025},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:50.835577Z"},"links":{"cited_paper":"/paper/2509.03975","citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:769410b2d82b4806fbc530e21b64ad74fb8fee9adac881a238121787acd6ef61","observation_id":"47a0cc51-58c8-499e-94bc-403cf8491e22","resolution":{"observed_at":"2026-08-05T10:33:56.710663Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2509.03975/citation-record","integrity":"/paper/2509.03975/integrity","json":"/paper/2509.03975/citation-record.json","paper":"/paper/2509.03975"},"outbound":[{"citation":{"cited_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"cited_work":{"arxiv_id":"2509.03975","doi":null,"metadata_source":"pith","pith_arxiv_id":"2509.03975","snapshot_observed_at":"2026-08-05T10:33:56.639846Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","venue":"cs.CV","work_id":"370dc768-157b-4c92-81a5-8a9cbc794f66","year":2025},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:50.835577Z"},"links":{"cited_paper":"/paper/2509.03975","citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:769410b2d82b4806fbc530e21b64ad74fb8fee9adac881a238121787acd6ef61","observation_id":"47a0cc51-58c8-499e-94bc-403cf8491e22","resolution":{"observed_at":"2026-08-05T10:33:56.710663Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:06.201088Z","title":null,"venue":null,"work_id":"913c779d-ac73-4d27-861e-2bc376243071","year":null},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.016978Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:e0531ab26bd38bbe4aae9c50341b0cf70571328f86bf01013d1ca9f6fd89c6cb","observation_id":"b9712390-d34c-4c33-885d-5fcea87306f4","resolution":{"observed_at":"2026-08-05T10:34:06.274702Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:06.001581Z","title":null,"venue":null,"work_id":"7f96b913-0ae9-4453-82c9-158b4dc885eb","year":null},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.130412Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:35153d8a73a745da67ba9f7e11fc9da4cf20e48403f644af2dd2bbc3fe425b2a","observation_id":"8252dd14-cc91-4dd7-a5c6-13434f5e9a4e","resolution":{"observed_at":"2026-08-05T10:34:06.068947Z","resolver_source":"raw_fallback","status":"malformed_identifier"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.47379/ls20065","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:05.615895Z","title":"An auxiliary modality available only during training improves the segmentation accuracy of a Y- Net model when applied to new data, even if it is not avail- able during test time","venue":null,"work_id":"2fccdb23-9975-4138-b90a-e5fb1794b099","year":null},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.287941Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:98e7934f37cc4502ddba23b94dbb22a853b1aa9d20441e8b0e1e33c0321c4e2f","observation_id":"c163a277-8778-4e77-884b-2b2bc28e05c8","resolution":{"observed_at":"2026-08-05T10:34:05.721493Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:05.807782Z","title":"If less than8annotations are 6 Table 5","venue":null,"work_id":"1c9e24f3-cc0e-423d-ac60-4196ff841124","year":null},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.219645Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:8d78c7c4ec8958f6802b727d752a94aec52faf14d9f711222b8f70975d8ca0e2","observation_id":"fed7f729-920f-4c70-8acb-f499dab11392","resolution":{"observed_at":"2026-08-05T10:34:05.894144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:04.496210Z","title":"Does the functional liver imaging score derived from gadoxetic acid–enhanced MRI predict outcomes in chronic liver disease?,","venue":null,"work_id":"3044e6f6-d849-43b1-9a99-f88c4384703f","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.975725Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:71e2a320d90bde2c7b586927bae2191f80fbb28adeff56c625b7af5fa0784de8","observation_id":"5c92ced5-7920-438b-b636-7e8ae478e571","resolution":{"observed_at":"2026-08-05T10:34:04.610691Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:05.428566Z","title":"Revisiting the risks of MRI with gadolinium based contrast agents—review of literature and guidelines,","venue":null,"work_id":"eedece6b-d9d6-4409-b8d1-c76d1be170ef","year":2015},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.379884Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:c5c3fe0ecbc0bad68ccabe779c95d62f506a4a72d8879736ac907b319a69cfee","observation_id":"779f21eb-e18f-47ba-8c71-d25987b27b3e","resolution":{"observed_at":"2026-08-05T10:34:05.536380Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:05.289506Z","title":"Ash and nash,","venue":null,"work_id":"0fe5fd26-a632-4a6f-b851-1805186f1ee1","year":2011},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.476786Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:c109cd3d4c81081c192b4dfa596aa5c5a1a23e9070e8ab0d001b3221b15141b8","observation_id":"370c5b81-b115-42fd-a77c-3ba7ff42101a","resolution":{"observed_at":"2026-08-05T10:34:05.362785Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:05.081497Z","title":"Synergy between nafld and afld and potential biomarkers,","venue":null,"work_id":"2b81aedf-1838-4f60-8ee0-38491b93899a","year":2015},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.613477Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:1098574df849f389ccc7574be58ec23af8f6e872818002e7741fbac8f140fe03","observation_id":"34d0c02c-e57a-40c3-b99f-97d6c3fc9148","resolution":{"observed_at":"2026-08-05T10:34:05.190400Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:04.920378Z","title":"Hepatic vessels segmentation using deep learning and preprocessing enhancement,","venue":null,"work_id":"fc9a108a-101c-482e-b140-48c8e3f10db4","year":2023},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.756818Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:ccd2a2bf2bafffb24f75a1a3d2da33f1cafceaa1415cc3058dd1427ee26a5204","observation_id":"62d08f3b-1dd8-465f-b255-2dc90d62813b","resolution":{"observed_at":"2026-08-05T10:34:04.998814Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:04.702836Z","title":"An automated liver tumour segmenta- tion from abdominal ct scans for hepatic surgical plan- ning,","venue":null,"work_id":"4e6aba9e-d233-43ec-a7e0-3a16e25915c4","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:51.865958Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:10d65818043d7d50d1d26ba6fe4f3b2d00b80c338fb5bc91e1ad0c7c3e127e2e","observation_id":"ea095d13-6ebf-4e21-a577-ea6dde1624c1","resolution":{"observed_at":"2026-08-05T10:34:04.834273Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:03.262184Z","title":"Hepatic vessel segmentation using vari- ational level set combined with non-local robust statis- tics,","venue":null,"work_id":"595aa0bb-a7a3-4489-a95c-9469e2a11bb0","year":2017},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.486291Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:30b133f5712f792452ded528b3433ec98bfec3a2a6bac76f48e90cefd3e7f6ea","observation_id":"5def8d55-e577-4499-b294-fb3712ebe984","resolution":{"observed_at":"2026-08-05T10:34:03.369794Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:04.269265Z","title":"Compu- tational methods for liver vessel segmentation in med- ical imaging: A review,","venue":null,"work_id":"c2f29dd9-a9fb-45a7-883f-68574cee25f1","year":2027},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.069473Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:c935d48777bc509820c63e53c144dba013f61dadc98d0b5e2193d0fe7be6a9f7","observation_id":"94fa80e5-d518-4a9b-98ca-49dd0b6f00a7","resolution":{"observed_at":"2026-08-05T10:34:04.365478Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:04.079265Z","title":"Multiscale vessel enhance- ment filtering,","venue":null,"work_id":"9585d452-b92c-479a-88a9-52a844af3c9b","year":1998},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.173708Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:976030294cd35940b7fb441fb2f6e9e7dc952c3fe434eb56bcb63b9c10ac33b1","observation_id":"8010b59f-1897-44b6-be39-aaaa742ed29a","resolution":{"observed_at":"2026-08-05T10:34:04.184591Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:03.865758Z","title":"Three-dimensional multi-scale line filter for segmentation and visualization of curvilinear structures in medical images,","venue":null,"work_id":"f1eca594-944a-4951-9bcc-d0098e5812bf","year":1998},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.267601Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:91a9ef0f4ed93c2c0a0dcee29546213e151f15d038ae6c4e75fef6b51718e7d5","observation_id":"aab67e07-6e11-4e55-95f3-ee21b77f4fbb","resolution":{"observed_at":"2026-08-05T10:34:03.969971Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:03.688034Z","title":"Design and validation of a tool for neurite tracing and analysis in fluorescence mi- croscopy images,","venue":null,"work_id":"808c2c70-c78e-4025-a02a-e1d92275fac0","year":2004},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.371329Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:b995f4a30ca2251e0c4e513f4de88f9284ca413c3fd16ed063ffccf9ece519e6","observation_id":"fff800c6-18bc-418a-974a-df0df21f5f5c","resolution":{"observed_at":"2026-08-05T10:34:03.751587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:03.473761Z","title":"Retinal ves- sel segmentation using the 2-d gabor wavelet and su- pervised classification,","venue":null,"work_id":"e60d8d1e-0570-412d-8f0e-011e01d43fea","year":2006},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.450749Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:bb67d7ff18df9d52876b65957ffd25209615e3e22b82f649e2f52134e3587861","observation_id":"9ed56068-c0d6-4ce8-82b3-e290b75e98d3","resolution":{"observed_at":"2026-08-05T10:34:03.569454Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:02.140330Z","title":"Robust liver vessel extraction us- ing 3d u-net with variant dice loss function,","venue":null,"work_id":"c383c60b-1d68-4f24-8808-1ad5e4976c84","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.950393Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:5ee40af5c3ef873c5577a2fa16cb7b81de9b8525428fe9635ff281a5940e30e7","observation_id":"bc46b22f-403b-47c6-b6b1-b38e7906760d","resolution":{"observed_at":"2026-08-05T10:34:02.243076Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:03.046271Z","title":"Au- tomatic liver vessel segmentation using 3d region grow- ing and hybrid active contour model,","venue":null,"work_id":"d01a2412-bf4c-4cc7-a4d0-01c07a1da683","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.553900Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:d278e106f96862211557d1b8ca3a6e621118f49851554256c4d1a7dcab919ba6","observation_id":"6fe5f6b2-223a-4ec0-ab36-79806dfb16df","resolution":{"observed_at":"2026-08-05T10:34:03.162999Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:02.841857Z","title":"Accurate liver vessel segmentation via active contour model with dense vessel candidates,","venue":null,"work_id":"618c900e-cc49-498b-a1d2-fdd55553b462","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.606162Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:bbcd491f82c09c044d93cbce625f13ece7633f06b84e393311b581bf98124eb4","observation_id":"c93e75d7-7e77-4044-9ab3-ad27a1d8477e","resolution":{"observed_at":"2026-08-05T10:34:02.940477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:02.649355Z","title":"Automatic segmentation methods for liver and hepatic vessels from ct and mri volumes, applied to the couinaud scheme,","venue":null,"work_id":"1c591f88-19d7-4a56-9d53-816cd46a39ff","year":2019},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.691071Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:8a2cf8acdcaf19b60709dc63f35c0f2b4b31a8e87535f978587d6af838e2d321","observation_id":"d56d3ec2-976d-4ce0-bee5-705369778044","resolution":{"observed_at":"2026-08-05T10:34:02.729144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:02.482672Z","title":"A novel method to model hepatic vascular network using vessel segmenta- tion, thinning, and completion,","venue":null,"work_id":"326d1d25-c640-4aa1-a175-2902ed2bf374","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.818318Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:6a7131aa23495b650e91a1ff787a4ee0048983fe8ddebd65c50614feae56ba4f","observation_id":"fe20b54b-5b68-498d-b9b8-421d10d13423","resolution":{"observed_at":"2026-08-05T10:34:02.573097Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:02.333833Z","title":"Combining deep learning with anatomical analysis for segmentation of the portal vein for liver sbrt planning,","venue":null,"work_id":"5b7a6caf-25d0-4a77-b9be-09131f9f4462","year":2017},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:52.874256Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:e36a7aebfe7fd706f1063dc5ea0aced22b68c8377d60a78f211adb21c77830a6","observation_id":"79a464aa-de9b-499b-ac52-534952e5178c","resolution":{"observed_at":"2026-08-05T10:34:02.402752Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:01.011928Z","title":"Training liver vessel segmentation deep neural net- works on noisy labels from contrast ct imaging,","venue":null,"work_id":"24924582-8273-4e6f-bc03-cfe5f62f0add","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.425187Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:b405deff4206f83b21c5545587c940f244db2eaca415115f161c894a599d50c9","observation_id":"5d3d0026-e69d-40b9-9465-ec0d92dee983","resolution":{"observed_at":"2026-08-05T10:34:01.104605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:01.931991Z","title":"Segmen- tation of vascular regions in ultrasound images: A deep learning approach,","venue":null,"work_id":"6b0362b9-3fd6-48bf-a476-f068898f8cba","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.016170Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:87228ca0e6945cbd272019893a00a00556e472b3309899745a9cb3408201bbd6","observation_id":"30a2bef3-3113-432d-8d7a-4b9326e00a57","resolution":{"observed_at":"2026-08-05T10:34:02.032927Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:01.766714Z","title":"Vesselnet: A deep convo- lutional neural network with multi pathways for robust hepatic vessel segmentation,","venue":null,"work_id":"14ed1388-d06b-44e3-97c9-58c2c9783a91","year":2019},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.145063Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:e2efbe18ddfc5b7468d1496869a8ece34f30579de7614be7a9ac25f5821dc449","observation_id":"6e475748-2eeb-4749-a311-f8b52c9e9ff8","resolution":{"observed_at":"2026-08-05T10:34:01.841490Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:06.386662Z","title":null,"venue":null,"work_id":"473b478d-9981-45ce-9ba8-242f54670aaf","year":null},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:50.912546Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:627e85212c26b1707248792c4cae61c5e810f4330ea18e712c37d4e059a95aeb","observation_id":"ae31d05f-93b6-43d8-ae9c-9eaf87cbc9d2","resolution":{"observed_at":"2026-08-05T10:34:06.503029Z","resolver_source":"raw_fallback","status":"unresolved"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:01.614246Z","title":"Topnet: Topol- ogy preserving metric learning for vessel tree recon- struction and labelling,","venue":null,"work_id":"eb89692f-1ee7-495a-8c7f-50c3d604a15f","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.214851Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:d3b9bd28a91313cc62e5c31dbe937fae818b8c2f0154c03dde89a9e4d84da42a","observation_id":"dfcf2713-79dd-46b3-b682-296a92f1c6fc","resolution":{"observed_at":"2026-08-05T10:34:01.688098Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:01.442525Z","title":"Mr-to-us reg- istration using multiclass segmentation of hepatic vas- culature with a reduced 3d u-net,","venue":null,"work_id":"8bc13cf8-f5bc-44ad-ba99-09e0ddf3f0e5","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.290174Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:c6ee55d7e5a5a5387370732e1c7cda91ebac677c8d3bc47b1990529754a21935","observation_id":"239132fe-f726-44a0-9ff2-4a822b86d18d","resolution":{"observed_at":"2026-08-05T10:34:01.518983Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:01.222804Z","title":"An attention-guided deep neu- ral network with multi-scale feature fusion for liver ves- sel segmentation,","venue":null,"work_id":"b73da73f-d7fd-45d6-a10a-2872b3158489","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.354028Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:2fada7f10dbfaf8b2a83fefbd513b04412066923ddde082b19f73773a79d3cc3","observation_id":"6465ed4c-58a8-4453-afee-b9b54aec8695","resolution":{"observed_at":"2026-08-05T10:34:01.326072Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:00.803637Z","title":"Segmentation of hepatic vessels from MRI images for planning of electroporation-based treatments in the liver,","venue":null,"work_id":"ebada54a-b675-4d84-932f-b626f65ccce4","year":2014},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.543314Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:3b3f4f20eaad6eb3bf995679eeadfac3abe603c440dd119335b04e9200262724","observation_id":"66aea506-8696-4883-9fa9-b61845ea6542","resolution":{"observed_at":"2026-08-05T10:34:00.871096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:00.594198Z","title":"Vessel segmentation from abdominal magnetic reso- nance images: adaptive and reconstructive approach,","venue":null,"work_id":"ece38e75-ca4e-4128-825a-6a29cfcb5502","year":2017},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.634062Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:eae07e9b7143dd387023c41c50caa0e77f2e3c75099cad88906cbe5d76b8890e","observation_id":"1aaf3ab1-b5d7-4586-98f7-fbce5b7832fa","resolution":{"observed_at":"2026-08-05T10:34:00.695515Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:00.415619Z","title":"3d u-net: learning dense volumetric segmentation from sparse annotation,","venue":null,"work_id":"5965bf98-e749-418f-b4ed-8f34ab70d34a","year":2016},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.754003Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:46f0bd4b610c0298b5253c445a3ab5be13338dc551fc4176c836da92630f5f20","observation_id":"420989b4-fc4e-4ee6-928a-509c90e61221","resolution":{"observed_at":"2026-08-05T10:34:00.491091Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:00.192966Z","title":"Multitask learning,","venue":null,"work_id":"81bda848-8603-43dd-a1c8-7ca1bf3ef9a4","year":1997},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:53.897637Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:7df1e14d0e23819b796864a9ca5760a580dae83e983bedce8eec3703f002946c","observation_id":"3b2df992-7504-4163-9d15-3fbdaa420e2c","resolution":{"observed_at":"2026-08-05T10:34:00.304270Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:34:00.014375Z","title":"A survey on multi-task learning,","venue":null,"work_id":"00203f5c-9bd1-4a01-913e-e9b00ff9874c","year":2021},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.016351Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:f165abaa04aeb5e4a4d9a3df00835c15da559bf565ac982cb005c051329be685","observation_id":"b42aa8f1-42f2-43e6-af41-6196c52dafef","resolution":{"observed_at":"2026-08-05T10:34:00.082381Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1905.07553","last_updated":"2020-09-03T00:03:26Z","snapshot_observed_at":"2026-08-11T13:52:26.342126Z","submitted_at":"2019-05-18T08:20:14Z","title":"Which Tasks Should Be Learned Together in Multi-task Learning?","version":4},"cited_work":{"arxiv_id":"1905.07553","doi":null,"metadata_source":"pith","pith_arxiv_id":"1905.07553","snapshot_observed_at":"2026-08-05T10:33:56.451113Z","title":"Which Tasks Should Be Learned Together in Multi-task Learning?","venue":"cs.CV","work_id":"2a349402-5390-4107-a3d9-d3a75f9c9af2","year":2019},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.186393Z"},"links":{"cited_paper":"/paper/1905.07553","citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:b59c46684772a984b754f1fb92a22310d11f949ed39fe952dc58be715761f75f","observation_id":"5828f00c-c2f9-4bf4-a4a0-756b4dbf6dca","resolution":{"observed_at":"2026-08-05T10:33:56.562183Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:59.878281Z","title":"Multi- task learning for brain tumor segmentation,","venue":null,"work_id":"516a77b6-9ecd-4616-9103-8c718b144588","year":2019},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.280366Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:d075bc6c083d39744ab9effb94cbb6560e7d618d6d4af7a966285e5b308c0f07","observation_id":"55891b9b-7db7-4b93-a633-715c7b4fd667","resolution":{"observed_at":"2026-08-05T10:33:59.933736Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:59.695541Z","title":"Multi-task deep learning based CT imag- ing analysis for covid-19 pneumonia: Classification and segmentation,","venue":null,"work_id":"6d18e26e-d88c-4e1d-8495-908dcfcb62e2","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.354826Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:ff379896e0dbf5e68fdf01b8f78619d547ab0a19c52a50a325f514e037da4db8","observation_id":"51c08f53-29ab-4c2c-b4e6-e7f5be26f7ac","resolution":{"observed_at":"2026-08-05T10:33:59.788464Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1707.08114","last_updated":"2021-03-29T14:32:58Z","snapshot_observed_at":"2026-08-09T11:29:20.378388Z","submitted_at":"2017-07-25T04:43:47Z","title":"A Survey on Multi-Task Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1707.08114","snapshot_observed_at":"2026-08-05T10:33:54.495513Z","title":"A survey on multi-task learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.495513Z"},"links":{"cited_paper":"/paper/1707.08114","citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:695ff9ad644e70b79bedd24ac02786314bfed0cdb6c32894e44ac84688b0fc46","observation_id":"d499ff4f-34ea-4a1e-ae26-de75af21cb6d","resolution":{"observed_at":"2026-08-05T10:33:54.495513Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:59.528535Z","title":"Cross-stitch networks for multi-task learning,","venue":null,"work_id":"98fab7ce-703b-4ef2-a7eb-1ed9a6ce5f88","year":2016},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.585852Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:9dda75d731e2d330cb39df903f827a84f7803a5488e1d265bbeb4762d2744f24","observation_id":"6ea5eaa0-b1d3-41bb-8a3d-a203f2d21a8e","resolution":{"observed_at":"2026-08-05T10:33:59.602364Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"2004.13379","last_updated":"2021-01-24T18:56:09Z","snapshot_observed_at":"2026-07-06T09:15:49.298623Z","submitted_at":"2020-04-28T09:15:50Z","title":"Multi-Task Learning for Dense Prediction Tasks: A Survey","version":3},"cited_work":{"arxiv_id":"2004.13379","doi":null,"metadata_source":"pith","pith_arxiv_id":"2004.13379","snapshot_observed_at":"2026-08-05T10:33:56.190421Z","title":"Multi-Task Learning for Dense Prediction Tasks: A Survey","venue":"cs.CV","work_id":"7184a5e2-8e13-4cd4-9637-451fc55f7f04","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.668420Z"},"links":{"cited_paper":"/paper/2004.13379","citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:456cc886a7e922baf41912601fb41531b92b620d9f67c35c403f74af170ee734","observation_id":"bdd55c9d-9100-488b-a672-8f6769e29a6e","resolution":{"observed_at":"2026-08-05T10:33:56.294059Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:59.313585Z","title":"Multi- task learning using uncertainty to weigh losses for scene geometry and semantics,","venue":null,"work_id":"f13adaa0-e69c-4c12-96d6-a4e0e3b020ce","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.735049Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:c58bed2aecbf29ba0a830b0772d04c68418d452efa2ae5498e5fff89cd2e828e","observation_id":"ac81542e-739c-4d16-a393-c678d6839654","resolution":{"observed_at":"2026-08-05T10:33:59.419508Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:59.096417Z","title":"Gradnorm: Gradient normaliza- tion for adaptive loss balancing in deep multitask net- works,","venue":null,"work_id":"b40b5ae8-b374-4f8f-a4ac-5b41bab0e8d6","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.832699Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:d5ec1805e7f9f77458fae6e8317b7b24dc70df805a833ec8b14bedbadb9b8dc3","observation_id":"dc12704e-29a4-4d59-91e1-5f07c986f162","resolution":{"observed_at":"2026-08-05T10:33:59.208413Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:58.893694Z","title":"Dynamic task prioritization for multitask learning,","venue":null,"work_id":"c3d39341-39de-4aaa-a8ed-a95897e708b6","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.918976Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:e783844da26f6c19d8ff7b44fe3086d0bf4be5646410d3fa2387567b39fe2d7b","observation_id":"b52c5b00-dbe5-4be7-ad8c-b605316fd9b8","resolution":{"observed_at":"2026-08-05T10:33:58.992715Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:58.708923Z","title":"End-to-end multi-task learning with attention,","venue":null,"work_id":"19fe7329-d365-4c6e-ae5b-5396cb34b733","year":2019},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:54.990693Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:acc96478b88ed9882be2614bb45925b069459ae9f6839f1bfdf8899c865e867b","observation_id":"1ca90183-cfbc-40a1-8bb1-2f93d9e13286","resolution":{"observed_at":"2026-08-05T10:33:58.809189Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:58.502608Z","title":"Joint left atrial segmentation and scar quantification based on a dnn with spatial encoding and shape at- tention,","venue":null,"work_id":"70bc96b3-9a46-461c-bf83-9f3cd14bab9c","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.125227Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:4e7ed0eac90a6849f67a3438980162a621c60c6d9d705330660b7af3cc52ac8b","observation_id":"398db62a-bda4-4571-a659-007c95b92a99","resolution":{"observed_at":"2026-08-05T10:33:58.637440Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:58.263068Z","title":"Y-net: a one-to-two deep learning framework for digital holographic reconstruction,","venue":null,"work_id":"f08828aa-8b55-4f91-b48d-760b630adbc7","year":2019},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.223544Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:288600f82a9629b4b5bd56c366eec76c8b78f6f84031c3de498211d2a697652b","observation_id":"d78d64bd-c836-4c15-961d-190a30a81aac","resolution":{"observed_at":"2026-08-05T10:33:58.382786Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:58.068786Z","title":"Nddr-cnn: Layerwise feature fusing in multi- task cnns by neural discriminative dimensionality re- duction,","venue":null,"work_id":"12e3566f-1486-4377-a759-3ca84a0f040b","year":2019},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.321785Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:7e46698557ad2f4af25c9575f5a7b0ddba17229fff2f48435cceed7f1a2d10df","observation_id":"34d4b246-7c35-4844-820c-00c01e625dee","resolution":{"observed_at":"2026-08-05T10:33:58.157292Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:57.885752Z","title":"Group normalization,","venue":null,"work_id":"fe814d15-218d-4232-abc1-5a178bee9620","year":2018},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.378840Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:c75267d10fd85cc12b6a8c7565a1f6c5fac2a65d6397f5d9adece3d0bbeb5982","observation_id":"d6be0ffc-85c0-472a-bb39-7d867c365004","resolution":{"observed_at":"2026-08-05T10:33:57.980312Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:57.707990Z","title":"Building skeleton models via 3-d medial sur- face axis thinning algorithms,","venue":null,"work_id":"47c0ec01-85a3-4fcb-afd6-9fdeb0f253b8","year":1994},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.435508Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:c893c8b9277b6965e4cb35cbba8f46ee93ef74e0da9ebd47262639b2c79f89b9","observation_id":"a8f16a1d-cd2d-40eb-9cd8-7e45ec4d2f70","resolution":{"observed_at":"2026-08-05T10:33:57.764274Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6980","last_updated":"2017-01-30T01:27:54Z","snapshot_observed_at":"2026-07-06T02:11:23.670680Z","submitted_at":"2014-12-22T13:54:29Z","title":"Adam: A Method for Stochastic Optimization","version":9},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6980","snapshot_observed_at":"2026-08-05T10:33:55.538344Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.538344Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:98722b3f53b1d17295542f2269e19ecffeb4cfe506551c71e1e0ca622363d8bd","observation_id":"ca13cc60-ac26-4ffb-82d7-5cd01881f7ad","resolution":{"observed_at":"2026-08-05T10:33:55.538344Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:57.494913Z","title":"A threshold selection method from gray-level histograms,","venue":null,"work_id":"8245c29b-38f5-4cb3-a7f6-cd340657dae2","year":1975},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.632872Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:d11ec0f6c9936dd896b364951e27ad5498e6d81592b0dfc392d58ff40c0f85af","observation_id":"cbc794ec-647c-40ca-9deb-df33dd3091c1","resolution":{"observed_at":"2026-08-05T10:33:57.597826Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1902.09063","last_updated":"2019-02-25T02:34:48Z","snapshot_observed_at":"2026-07-06T07:35:04.987332Z","submitted_at":"2019-02-25T02:34:48Z","title":"A large annotated medical image dataset for the development and evaluation of segmentation algorithms","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1902.09063","snapshot_observed_at":"2026-08-05T10:33:55.727933Z","title":"A large annotated 13 medical image dataset for the development and eval- uation of segmentation algorithms,","venue":null,"work_id":null,"year":1902},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.727933Z"},"links":{"cited_paper":"/paper/1902.09063","citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:194b20be0f4db46ffe6ceceae6f6629e72f6ce57d1d3385f95a258d5a53dba04","observation_id":"3ec91886-8b80-4f4b-abb3-0845389e5b38","resolution":{"observed_at":"2026-08-05T10:33:55.727933Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:57.284525Z","title":"Liver segment approximation in ct data for surgical resection planning,","venue":null,"work_id":"2914101b-a486-40af-82a3-b87066167308","year":2004},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.825513Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:3e15959ea36e2b15fa2a430992827e5234162dc3dd2a5da6497883949bb4c3de","observation_id":"36ab1eea-99eb-4d9f-a99c-c9bf3a14f2a6","resolution":{"observed_at":"2026-08-05T10:33:57.377949Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:57.015531Z","title":"Vascular branching ge- ometry relating to portal hypertension: a study of liver microvasculature in cirrhotic rats by x-ray phase- contrast computed tomography,","venue":null,"work_id":"b4bfa65e-4f96-411a-972f-a065692345a4","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:55.915876Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:e2e6f8a537c135ffb1eb926bd417fb1deb0c6cb6a3ec5daa14694f2d14d2f9af","observation_id":"1dcceff4-1994-4e4a-a940-c0674da7c864","resolution":{"observed_at":"2026-08-05T10:33:57.150096Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-05T10:33:56.802566Z","title":"Accurate and ver- satile 3d segmentation of plant tissues at cellular resolu- tion,","venue":null,"work_id":"98f05d47-a71e-451e-a1a0-710005cf0f7d","year":2020},"citing_paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-05T10:33:56.021465Z"},"links":{"citing_paper":"/paper/2509.03975"},"observation_digest":"sha256:e174e56da1084d635f6bb1c73b03f3c8fa46c00a8bc337660eb56f373013e644","observation_id":"cab83541-81b7-4e29-89bf-25544c82b061","resolution":{"observed_at":"2026-08-05T10:33:56.902961Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-11T06:34:44.6726+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2509.03975","last_updated":"2025-09-05T07:22:43Z","latest_version":2,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T11:29:47.224977Z","submitted_at":"2025-09-04T08:01:27Z","title":"Improving Vessel Segmentation with Multi-Task Learning and Auxiliary Data Available Only During Model Training"},"reference_resolution":{"displayed":56,"state_counts":{"malformed_identifier":1,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":5,"verified_exact":4,"verified_fuzzy":46},"total_outbound_references":56},"refusal":"A citation records a reference. It does not transfer a finding from one paper to another.","schema":"pith.paper-citation-record.v1","standing_sources":[{"observed_at":"2026-08-11T06:34:44.6726+00:00","source":"crossref"},{"observed_at":"2026-08-11T06:34:36.301508+00:00","source":"retraction_watch"}],"thesis":"As of 11 August 2026, this Paper Citation Record lists 56 of 56 outbound references and 1 inbound Pith citation observation for arXiv:2509.03975."}