{"as_of":"2026-08-16T19:03:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:a5020735d221cb0aff8d06ada934c5a3b681e97c00d75b1104e98e98f9026610","coverage":[{"denominator":35,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":35,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-14T12:54:04.174982Z","state":"measured"},{"denominator":35,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":35,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-16T06:30:59.297886+00:00","state":"measured"},{"denominator":0,"lane":"inbound_itemization","note":"Pith citing papers itemized under the disclosed page cap.","records_observed":0,"source":"paper_references, paper_reference_links","source_observed_at":null,"state":"measured"},{"denominator":1,"lane":"external_citation_measurements","note":"A source-named dated measurement, never combined with another source.","records_observed":0,"source":"cited_works","source_observed_at":null,"state":"measured"}],"external_citation_measurements":[],"inbound":[],"links":{"evidence":"/evidence","html":"/paper/1908.06337/citation-record","integrity":"/paper/1908.06337/integrity","json":"/paper/1908.06337/citation-record.json","paper":"/paper/1908.06337"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:54:03.995656Z","title":"Tensorﬂow: a system for large-scale machine learning","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:03.995656Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:aa4f819bbd974c514e85e624fcc7afbce4dd2f61a3e1377b56eb36488abaf692","observation_id":"f36f0100-d1b5-41ae-9833-5370284cc1e1","resolution":{"observed_at":"2026-08-14T12:54:03.995656Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1812.11302","last_updated":"2018-12-29T07:22:57Z","snapshot_observed_at":"2026-08-14T17:37:16.830273Z","submitted_at":"2018-12-29T07:22:57Z","title":"Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks","version":1},"cited_work":{"arxiv_id":"1812.11302","doi":null,"metadata_source":"pith","pith_arxiv_id":"1812.11302","snapshot_observed_at":"2026-08-14T12:54:04.411992Z","title":"Annotation-cost Minimization for Medical Image Segmentation using Suggestive Mixed Supervision Fully Convolutional Networks","venue":"cs.CV","work_id":"89a08df5-8527-46e6-88d5-a92a2e0819b9","year":2018},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.003797Z"},"links":{"cited_paper":"/paper/1812.11302","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:dd54bdbf6a88e3906f216dafb4516de551429ca60341ae28c505dc55d615c39f","observation_id":"e707a883-4a92-44d7-9059-82c30e2aed75","resolution":{"observed_at":"2026-08-14T12:54:04.417722Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.843742Z","title":"Bhatia, Positive deﬁnite matrices","venue":null,"work_id":"4ef5d5ad-06c4-4485-a9ae-ec9fbdeb55d3","year":2009},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.009629Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:93e3380c5ab06338be984b2a424baaf5587e44310ae35673dccf81212656237e","observation_id":"eac664fe-d634-4ec5-97c4-db73357224f7","resolution":{"observed_at":"2026-08-14T12:54:04.848919Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.828295Z","title":"Sensitive quantitative predictions of peptide-mhc binding by a ‘query by com- mittee’artiﬁcial neural network approach,","venue":null,"work_id":"a205c9fb-cf38-4d67-857d-199e568a656e","year":2003},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.014791Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:a5e02713c840f39437dde2ae3622eae9ea23462f06f91f8f52f045edf1f34c35","observation_id":"3607efb5-a0fa-4273-b0af-c0e999aed51f","resolution":{"observed_at":"2026-08-14T12:54:04.833356Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:54:04.020669Z","title":"Chollet et al., “Keras,” 2015","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.020669Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:6e914c64d688e51c30c0a8087bdedb6f92ac27596b4ad0e307df0e3430ca146f","observation_id":"dc7c8e96-31da-4cd4-bb82-58270b40cb03","resolution":{"observed_at":"2026-08-14T12:54:04.020669Z","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":"2026-08-14T12:54:04.803321Z","title":"Incorpo- rating expert feedback into active anomaly discovery,","venue":null,"work_id":"da8b3061-4e30-4509-96ae-fc8d6b19290c","year":2016},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.025889Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:f95fd43cf05993a0ddc92eb505ec21889a3a92dfcb93d9d017925b9e9cdbe0fa","observation_id":"52e80716-8d53-4d86-bea7-9c2d9c6a9da4","resolution":{"observed_at":"2026-08-14T12:54:04.807748Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.07510","last_updated":"2018-07-28T03:44:09Z","snapshot_observed_at":"2026-08-14T18:49:54.284040Z","submitted_at":"2018-07-19T16:02:41Z","title":"A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net","version":4},"cited_work":{"arxiv_id":"1807.07510","doi":null,"metadata_source":"pith","pith_arxiv_id":"1807.07510","snapshot_observed_at":"2026-08-14T12:54:04.376021Z","title":"A Strategy of MR Brain Tissue Images' Suggestive Annotation Based on Modified U-Net","venue":"cs.CV","work_id":"b9c1ec48-e1d6-4aa0-95a1-99d6f34176d2","year":2018},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.031678Z"},"links":{"cited_paper":"/paper/1807.07510","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:1ee6786c568a3d85fd353b646ed6436542f1abe274183c13bbced0a9b44812e7","observation_id":"e48b643f-ed16-472d-9d18-a4f7cd853d0b","resolution":{"observed_at":"2026-08-14T12:54:04.383054Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1807.00502","last_updated":"2018-07-02T07:42:51Z","snapshot_observed_at":"2026-08-14T18:56:52.277121Z","submitted_at":"2018-07-02T07:42:51Z","title":"Leveraging Uncertainty Estimates for Predicting Segmentation Quality","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1807.00502","snapshot_observed_at":"2026-08-14T12:54:04.036914Z","title":"Leveraging uncertainty estimates for predicting segmentation quality,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.036914Z"},"links":{"cited_paper":"/paper/1807.00502","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:43e1ea923f9f57952dbc98241f86bae33e7b92cc701d84c09769fe034930840a","observation_id":"3ec71ec6-8b7f-419f-99e6-83c11a92827e","resolution":{"observed_at":"2026-08-14T12:54:04.036914Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1907.05143","last_updated":"2019-07-11T12:31:30Z","snapshot_observed_at":"2026-08-05T09:46:53.623320Z","submitted_at":"2019-07-11T12:31:30Z","title":"Deep Active Learning for Axon-Myelin Segmentation on Histology Data","version":1},"cited_work":{"arxiv_id":"1907.05143","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.05143","snapshot_observed_at":"2026-08-14T12:54:04.333173Z","title":"Deep Active Learning for Axon-Myelin Segmentation on Histology Data","venue":"cs.CV","work_id":"43f99a23-4dbe-40d3-8834-0a81243e24b8","year":2019},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.042166Z"},"links":{"cited_paper":"/paper/1907.05143","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:c05d5345d6c417d6b416c3e543b3d634c300f939c0ff4b11a974537ca0af45a6","observation_id":"216b8923-0d44-4e72-b3af-384e24eb88a7","resolution":{"observed_at":"2026-08-14T12:54:04.339284Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1712.05319","last_updated":"2017-12-19T16:54:19Z","snapshot_observed_at":"2026-08-14T20:03:37.406754Z","submitted_at":"2017-12-14T16:27:42Z","title":"Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation","version":2},"cited_work":{"arxiv_id":"1712.05319","doi":null,"metadata_source":"pith","pith_arxiv_id":"1712.05319","snapshot_observed_at":"2026-08-14T12:54:04.307964Z","title":"Deep CNN ensembles and suggestive annotations for infant brain MRI segmentation","venue":"cs.CV","work_id":"e2056ecf-1350-4822-b68e-cc99a2df9ec3","year":2017},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.047224Z"},"links":{"cited_paper":"/paper/1712.05319","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:efbff3169bf643a0a292642e3a74742b115b53a053cb446cfe51f2f85318ece3","observation_id":"c831d884-d178-4850-a560-b5eb8b63b685","resolution":{"observed_at":"2026-08-14T12:54:04.313945Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.786849Z","title":"Selective sampling using the query by committee algorithm,","venue":null,"work_id":"262b4848-f97a-49e7-937d-1704ba23bdde","year":1997},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.051947Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:634f26d36819de8ca261e2fdf6984e3248b4e2700b6258152b7a7831ddce35cb","observation_id":"114eaf8d-1179-43bf-8b8e-6d1a6403eb04","resolution":{"observed_at":"2026-08-14T12:54:04.791850Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1810.01621","last_updated":"2019-09-06T22:45:17Z","snapshot_observed_at":"2026-08-14T18:20:10.895634Z","submitted_at":"2018-10-03T08:10:35Z","title":"Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?","version":3},"cited_work":{"arxiv_id":"1810.01621","doi":null,"metadata_source":"pith","pith_arxiv_id":"1810.01621","snapshot_observed_at":"2026-08-14T12:54:04.276827Z","title":"Extreme Augmentation : Can deep learning based medical image segmentation be trained using a single manually delineated scan?","venue":"cs.CV","work_id":"6d8bf7b4-6426-48b3-af0d-44b7d08e6fd8","year":2018},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.056484Z"},"links":{"cited_paper":"/paper/1810.01621","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:875cb074bf55875226781aa1f7a73ad473e59200b4721ce1bd9abf755434c43a","observation_id":"d00b421f-6d56-4c9c-85d9-29b754ab205d","resolution":{"observed_at":"2026-08-14T12:54:04.283959Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.770212Z","title":"Deep learning in the small sample size setting: cascaded feed forward neural net- works for medical image segmentation,","venue":null,"work_id":"8df28503-483a-491c-9fa2-d5b3310af9a0","year":2016},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.062290Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:7c1b6d32a11eb9b734a10b29810fbf18e879570b6f568d4951c26e0ccc843bfe","observation_id":"7536f309-e68b-4716-a4ce-ea902fb8e442","resolution":{"observed_at":"2026-08-14T12:54:04.775630Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.754439Z","title":"Query by committee made real,","venue":null,"work_id":"69fb0de9-01b8-471c-9f5c-37f15c281a39","year":2006},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.067132Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:189625de0184a0eb66ebb59d65c1b9b943abcd292086abee7fa3def92fb2b8f9","observation_id":"66bff03a-dd95-4b03-a378-b79de312c0b5","resolution":{"observed_at":"2026-08-14T12:54:04.759460Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1412.6572","last_updated":"2015-03-20T20:19:16Z","snapshot_observed_at":"2026-08-12T17:13:46.394331Z","submitted_at":"2014-12-20T01:17:12Z","title":"Explaining and Harnessing Adversarial Examples","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1412.6572","snapshot_observed_at":"2026-08-14T12:54:04.072264Z","title":"Explaining and harness- ing adversarial examples,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.072264Z"},"links":{"cited_paper":"/paper/1412.6572","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:ca8a9a5aa4a9ba4b1344c30378c6e7462b6eaa488504811ce6ab2581b69323c2","observation_id":"6c968a5c-0bfa-4a46-9a53-8b303dfb63ad","resolution":{"observed_at":"2026-08-14T12:54:04.072264Z","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":"2026-08-14T12:54:04.738179Z","title":"Guest editorial deep learning in medical imaging: Overview and future promise of an exciting new technique,","venue":null,"work_id":"ad12a208-0907-416b-ae99-11871af7deb4","year":2016},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.077461Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:526cff79459aa8bfd514c97a4872f6d8075adef30721bbbe8778e81921c4305e","observation_id":"af343892-2e39-47a8-b1b3-398719a709ed","resolution":{"observed_at":"2026-08-14T12:54:04.743466Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1907.03338","last_updated":"2019-10-11T09:17:20Z","snapshot_observed_at":"2026-08-14T16:08:04.604770Z","submitted_at":"2019-07-07T19:36:28Z","title":"Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation","version":2},"cited_work":{"arxiv_id":"1907.03338","doi":null,"metadata_source":"pith","pith_arxiv_id":"1907.03338","snapshot_observed_at":"2026-08-14T12:54:04.233551Z","title":"Assessing Reliability and Challenges of Uncertainty Estimations for Medical Image Segmentation","venue":"eess.IV","work_id":"31ae90c6-2523-4784-9206-ac23249c53c8","year":2019},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.083606Z"},"links":{"cited_paper":"/paper/1907.03338","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:d14d091948a5774a594278bb684fee40a04223c3d9111ada6a355d6c26725d39","observation_id":"b55e2b81-8ab6-489c-a9ef-3d7af5a7dab9","resolution":{"observed_at":"2026-08-14T12:54:04.240571Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.720850Z","title":"Medical image ﬁle formats,","venue":null,"work_id":"a3d802ff-26f3-4e29-9504-aa9133277f14","year":2014},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.088750Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:c9c7598e5da8a1604d100d97c004a3417ee61bf23f7e60e38a86a1bc44f64378","observation_id":"cb6d1af4-3089-4073-8fc1-b15e777d1c40","resolution":{"observed_at":"2026-08-14T12:54:04.726587Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.702741Z","title":"The ﬁrst step for neuroimaging data analysis: DICOM to NIfTI conversion,","venue":null,"work_id":"1b4f9320-1e34-40c5-ab81-af16faebaf96","year":2016},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.093360Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:c1c6af73f45128530670aec71b58f3fbfc798b4b3132fa41ad6fc1d13ac15fc7","observation_id":"732e8cc8-46d1-4d38-ac4b-2346792ef175","resolution":{"observed_at":"2026-08-14T12:54:04.708545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.685540Z","title":"The design of simpleitk,","venue":null,"work_id":"fb517f87-9dfa-4611-8f5f-e21e98cd319c","year":2013},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.097971Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:11e306dec56211cd2f4e868eb05fa01d08bded1661840a3c18efd1a1acfe0d8d","observation_id":"e762a341-48b8-4735-974a-403c1c07c5ec","resolution":{"observed_at":"2026-08-14T12:54:04.690522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.670305Z","title":"On the positive semi-deﬁnite property of similarity matrices,","venue":null,"work_id":"f391668a-f3af-40f3-bf0b-12aa377eebb0","year":2019},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.103096Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:46a12956c4a33ccec7d9f4679e0cf835ed8b2e18bab4d2f0ea535dd18e1677f7","observation_id":"b4243f61-fe6c-48cf-819f-8a9d3dd5243a","resolution":{"observed_at":"2026-08-14T12:54:04.674948Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.654447Z","title":"Deep neural networks are easily fooled: High conﬁdence predictions for unrecognizable images,","venue":null,"work_id":"0faf8675-1cb6-4ad9-b275-3971820811eb","year":2015},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.108282Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:11f4a4310dc0dc23098c603151217a140008a32be49a8151a3291436045a8ce2","observation_id":"88c290f1-aa71-4f2c-9e7a-9066629e9db7","resolution":{"observed_at":"2026-08-14T12:54:04.659566Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.636587Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation,","venue":null,"work_id":"9fab106a-e6e0-46c8-bbe8-58fc197a8603","year":2015},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.113549Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:e299ddd2d5424b9e28f381b3ba8a2cd94d4e0e3632928bc10abec573884fbfce","observation_id":"c6bbc9fe-ae2c-48c9-b6a6-2f2e582d3536","resolution":{"observed_at":"2026-08-14T12:54:04.642842Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.614147Z","title":"Active learn- ing in recommender systems,","venue":null,"work_id":"75ddb33c-82e8-40e3-ac79-357353345533","year":2015},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.118710Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:efbc965b8cd9b7ce969cdb4b6efdd53c6dbe68b761d917f92444b244bcb4c519","observation_id":"163ad261-9550-4f7e-849f-5c1803470cbb","resolution":{"observed_at":"2026-08-14T12:54:04.619680Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1906.03543","last_updated":"2019-06-08T23:53:57Z","snapshot_observed_at":"2026-08-14T16:18:42.651108Z","submitted_at":"2019-06-08T23:53:57Z","title":"apricot: Submodular selection for data summarization in Python","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1906.03543","snapshot_observed_at":"2026-08-14T12:54:04.123690Z","title":"apricot: Submodu- lar selection for data summarization in python,","venue":null,"work_id":null,"year":1906},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.123690Z"},"links":{"cited_paper":"/paper/1906.03543","citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:535f56d8aa224f147533b639a9d0b04a81aa008e2abaac6266be4fa8fa46adfb","observation_id":"559ab0e2-92f2-4d75-9adc-ac1323aa3e6f","resolution":{"observed_at":"2026-08-14T12:54:04.123690Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-14T12:54:04.129514Z","title":"Active learning literature survey,","venue":null,"work_id":null,"year":2009},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.129514Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:39926abdc66a4db7f9d4e863ec65b224510667883ebd7e5b58c0189fb393bcdc","observation_id":"dfe131f2-ce6f-4bdd-8fcb-8f32ce9b112c","resolution":{"observed_at":"2026-08-14T12:54:04.129514Z","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":"2026-08-14T12:54:04.580538Z","title":"Query by committee,","venue":null,"work_id":"bbd10009-aa13-40f9-b706-0e310eed391c","year":1992},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.134691Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:afd01ca42f392361dac4c177b9f380c823b31bba64139fa7be934793f7ddff83","observation_id":"1e6a38e7-4a79-4beb-ad3d-482ee493a59e","resolution":{"observed_at":"2026-08-14T12:54:04.585986Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.556797Z","title":"Active deep learning with ﬁsher information for patch-wise semantic segmentation,","venue":null,"work_id":"cc35afd6-80f2-48cd-928f-ef7522ad32c1","year":2018},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.140490Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:dede94e60c5507da7a6fa70357eb69b8dc0313afecd884ff893bb5f40083d378","observation_id":"dc26a62d-f751-4be1-af97-61daa60216c1","resolution":{"observed_at":"2026-08-14T12:54:04.567226Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.540086Z","title":"Current procedural terminology (CPT)","venue":null,"work_id":"012c1eb1-24c6-4df3-a862-7669350e1dff","year":1970},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.145591Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:292a5477a266450f4f530f62d17add0f4dd4b22784f32558ba31a3154dd79312","observation_id":"b1d0358c-210b-4bd8-9d3e-89102f858943","resolution":{"observed_at":"2026-08-14T12:54:04.545558Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.523005Z","title":"Submodularity in data subset selection and active learning,","venue":null,"work_id":"738160f4-ce52-4ca9-9768-5c8b1c198d2b","year":2015},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.150259Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:44edaedc4bcba203311935027d1892a100c317ad7f4c2039fbceb907affced44","observation_id":"0743e671-bd49-4c93-95ad-6730416ae794","resolution":{"observed_at":"2026-08-14T12:54:04.527692Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.504255Z","title":"Suggestive annotation: A deep active learning framework for biomedical image segmentation,","venue":null,"work_id":"9c65dd83-6ac1-4c15-a53e-1d8730090023","year":2017},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.154986Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:524f281fc1c821945a24b9966c1e4e535b909d76f4c231589029c1567d4ba5cb","observation_id":"370b8603-3025-4a50-85c3-fae803dfd126","resolution":{"observed_at":"2026-08-14T12:54:04.510799Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.481043Z","title":"User-guided 3D active contour segmentation of anatomical structures: Signiﬁcantly improved efﬁciency and reliabil- ity,","venue":null,"work_id":"914ba807-7217-4d2e-93d9-2605d33ae29f","year":2006},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.160131Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:6918e189b6a756b91059c453cee66f3dd370291a79d7d984d5f1d64a48c7b515","observation_id":"8bee8ea9-cea3-4530-9d97-4de5ca96e420","resolution":{"observed_at":"2026-08-14T12:54:04.488470Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.463506Z","title":"Road extraction by deep residual u-net,","venue":null,"work_id":"69235486-ada9-4719-87de-55dfd6fdb078","year":2018},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.164761Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:1e33f6976e00ef2f4e4204212e7ee8220d3c79c7a7ed2ff3b52366ad7fbb2a14","observation_id":"2f9359ac-3b08-4673-b0fb-ec1d4e685045","resolution":{"observed_at":"2026-08-14T12:54:04.468605Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.446628Z","title":"Deep learning based instance segmentation in 3d biomedical images using weak annotation,","venue":null,"work_id":"ae05c8f9-2106-4a6d-8566-fcc89471e83a","year":2018},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.170026Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:c5802c893085b0162e0ee6b37463352b31c7609bc220ce6e337c8d2211971ba7","observation_id":"0fea2eab-77a8-44f3-9434-7473663c52f0","resolution":{"observed_at":"2026-08-14T12:54:04.452617Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+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":"2026-08-14T12:54:04.430384Z","title":"A brief introduction to weakly supervised learning,","venue":null,"work_id":"37c5d4c9-d921-4955-bb83-274d9f1459e2","year":2017},"citing_paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-14T12:54:04.174982Z"},"links":{"citing_paper":"/paper/1908.06337"},"observation_digest":"sha256:029fe6a24494f9784ed20ac944b4657744aba05f98773a099423cae875892e1a","observation_id":"6bffd6b4-a386-4ff5-b056-1366be1c7ba0","resolution":{"observed_at":"2026-08-14T12:54:04.435395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-16T06:30:59.297886+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"1908.06337","last_updated":"2021-01-18T19:40:32Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-16T07:59:44.656936Z","submitted_at":"2019-08-17T20:16:07Z","title":"EigenRank by Committee: A Data Subset Selection and Failure Prediction paradigm for Robust Deep Learning based Medical Image Segmentation"},"reference_resolution":{"displayed":35,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":6,"verified_exact":6,"verified_fuzzy":23},"total_outbound_references":35},"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":"2026-08-16T06:30:59.297886+00:00","source":"crossref"},{"observed_at":"2026-08-16T06:30:54.164669+00:00","source":"retraction_watch"}],"thesis":"As of 16 August 2026, this Paper Citation Record lists 35 of 35 outbound references and 0 inbound Pith citation observations for arXiv:1908.06337."}