{"as_of":"2026-08-13T06:15:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:2754763dabf466b23f683e13bed62ea4273d1ad95b7371fa3847da9440f817de","coverage":[{"denominator":43,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":43,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-12T13:06:18.887708Z","state":"measured"},{"denominator":44,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":44,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-12T06:34:41.77262+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":"2026-08-11T20:21:51.021917Z","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":"2026-08-11T20:21:51.141278Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"cited_work":{"arxiv_id":"2411.16515","doi":null,"metadata_source":"pith","pith_arxiv_id":"2411.16515","snapshot_observed_at":"2026-08-11T20:21:51.141278Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","venue":"eess.IV","work_id":"7524f4a9-ecaa-4efc-8289-122f5ff70a9f","year":2024},"citing_paper":{"arxiv_id":"2412.05833","last_updated":"2024-12-08T06:48:09Z","snapshot_observed_at":"2026-08-12T20:34:48.851505Z","submitted_at":"2024-12-08T06:48:09Z","title":"CSG: A Context-Semantic Guided Diffusion Approach in De Novo Musculoskeletal Ultrasound Image Generation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-11T20:21:51.021917Z"},"links":{"cited_paper":"/paper/2411.16515","citing_paper":"/paper/2412.05833"},"observation_digest":"sha256:d8eaebb169ba0db19e12985b7a3952ea553578a0d2cba562d4d842c8050d76d3","observation_id":"46a623c5-4c24-4c52-83ad-5143babf71a1","resolution":{"observed_at":"2026-08-11T20:21:51.146893Z","resolver_source":"local_arxiv","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2411.16515/citation-record","integrity":"/paper/2411.16515/integrity","json":"/paper/2411.16515/citation-record.json","paper":"/paper/2411.16515"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":"10.1007/978-3-030-99838-7_12","metadata_source":"doi_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-12T13:06:18.910906Z","title":null,"venue":null,"work_id":"18f6784d-debe-40f5-8ed9-e247360f62e1","year":2022},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.757820Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:bd92cd096a195fa14b0abaea66e3ab790964a47a62846cf21223659ae8c86878","observation_id":"eab93229-f808-4d1f-9d8a-e449ade0ffd3","resolution":{"observed_at":"2026-08-12T13:06:18.914649Z","resolver_source":"doi","status":"verified_exact"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.322430Z","title":"Classification of melanocytic lesions in selected and whole-slide images via convolutional neural networks,","venue":null,"work_id":"b40847e0-4b2d-4133-a654-c8414a169021","year":2019},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.761573Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:c2bd6ec0535b752ede6a4e3cb4240ce29912cae585b77dd72b0e2802f24f5fa1","observation_id":"521f99b8-0333-40d3-b91d-68ab3e5a0789","resolution":{"observed_at":"2026-08-12T13:06:19.326522Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.312629Z","title":"Segmentation methods of h&e-stained histological images of lymphoma: a review,","venue":null,"work_id":"1c7af623-7006-4daf-ac16-d98994af6526","year":2017},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.764794Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:e69f8cd540c111a99441ea82f5279de45f75337f98c93c3c28d547057d07cb7e","observation_id":"500757d3-5e3b-4d16-a5de-ccb7bc425821","resolution":{"observed_at":"2026-08-12T13:06:19.315779Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.303405Z","title":"A deep learning algorithm for one-step contour aware nuclei segmentation of histopathology images,","venue":null,"work_id":"8fe4adbe-5135-49ef-ad5e-b2d04e624e80","year":2027},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.767963Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:33d0b3eecd13f581a84668d394bc2cbcd9a10602835c2ab17d5e8b7f1c5a567b","observation_id":"aa07f7cc-2bba-430b-acb4-aa1bf88b6394","resolution":{"observed_at":"2026-08-12T13:06:19.306925Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.294152Z","title":"A deep learning approach for semantic segmenta- tion in histology tissue images,","venue":null,"work_id":"f3a77862-0bd4-499a-88b6-4af7ffec3c76","year":2016},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.771313Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:92110df8f3b56da4e7e107f3a40d07fe143b69bb6fc0925ae77a8ebfd7c08c0e","observation_id":"b58aeeab-d1d6-4e98-81b4-038a8fa17ede","resolution":{"observed_at":"2026-08-12T13:06:19.297505Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.285212Z","title":"Deep learning in big image data: Histology image classification for breast cancer diagnosis,","venue":null,"work_id":"c9357e30-c69c-43be-8a90-acb18ba6a85d","year":2016},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.774413Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:74e222569349ea3d2ca8447497f4206ed710f22d1d9ea85ffdcae0054c10629c","observation_id":"68868d83-7304-46f9-9642-22000529922c","resolution":{"observed_at":"2026-08-12T13:06:19.288434Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.276163Z","title":"Using deep learning to enhance cancer diagnosis and classification,","venue":null,"work_id":"7a42a557-cb3f-4dc6-8e10-d2bbaf8a061b","year":2013},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.777712Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:0b8eea266bd332753e605a0500cfcda3b16e08678ca55b33e3b1e2eda9f62c70","observation_id":"8ddd12fb-cd3c-462a-a016-f2e5ca3471aa","resolution":{"observed_at":"2026-08-12T13:06:19.279444Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1703.07047","last_updated":"2018-06-28T01:21:51Z","snapshot_observed_at":"2026-07-06T05:34:31.575182Z","submitted_at":"2017-03-21T04:11:13Z","title":"High-Resolution Breast Cancer Screening with Multi-View Deep Convolutional Neural Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1703.07047","snapshot_observed_at":"2026-08-12T13:06:18.780649Z","title":"High-resolution breast cancer screening with multi-view deep convolutional neural networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.780649Z"},"links":{"cited_paper":"/paper/1703.07047","citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:71c39855ad4c2822713e915e344b30f1f547a977c0d8fb6559a146c3aeacdf79","observation_id":"659b4256-72b3-4475-a37b-72d0eb4b4c4a","resolution":{"observed_at":"2026-08-12T13:06:18.780649Z","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":"2026-08-12T13:06:19.266373Z","title":"Precision histology: how deep learning is poised to revitalize histomorphology for personalized cancer care,","venue":null,"work_id":"c82c056a-6749-4e35-b393-7fe4b4577fb4","year":2017},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.783965Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:305646cfb552171355ebcbee257cb8ce90923147b477d64f9f1a1f57a8a61ac7","observation_id":"8b71862f-9cb7-4225-8c71-3e0f14d5eef3","resolution":{"observed_at":"2026-08-12T13:06:19.270102Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.256211Z","title":"Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis,","venue":null,"work_id":"a43917d7-3577-4707-8f83-50eb2ef68f7c","year":2022},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.786908Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:349cd85a1fa9dc84b2c5439c4997e214f4a2fd996f304f4f6f389cab41495546","observation_id":"2f2d1220-6271-42f2-a51e-1f5eb7e2efd1","resolution":{"observed_at":"2026-08-12T13:06:19.259862Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:18.789803Z","title":"Machine learning approach for biopsy-based identification of eosinophilic esophagitis reveals importance of global features,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.789803Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:fd1be0e3073c98543c9c3a7c3ffa2529eb277b21c4bd24627b0283034fa09f55","observation_id":"214fc180-0bcc-47e7-a6b2-4f5be3b0a184","resolution":{"observed_at":"2026-08-12T13:06:18.789803Z","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":"2026-08-12T13:06:18.792681Z","title":"A deep multi-label segmentation network for eosinophilic esophagitis whole slide biopsy diagnostics,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.792681Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:74596ad745ad06d755bcdb4dfdd7236424d79649a7101b987331af0975d03645","observation_id":"cc725d30-6340-42f1-96e8-34d4f7c4d549","resolution":{"observed_at":"2026-08-12T13:06:18.792681Z","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":"2026-08-12T13:06:19.236221Z","title":"Deep learning for digital pathology image analysis: A comprehensive tutorial with selected use cases,","venue":null,"work_id":"e8a40f05-3d6b-471e-8e05-9e54c80ab6cf","year":2016},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.795671Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:4ea6f7ad2250e98e1916a03e69d9450fc1513377f262b84cedce64a4ab6535ce","observation_id":"6936399c-b94f-410a-adcb-4fc1baa51b82","resolution":{"observed_at":"2026-08-12T13:06:19.239458Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.227100Z","title":"Deep learning in medical image analysis,","venue":null,"work_id":"a5db9345-4b4c-40e2-8416-36a677838a1e","year":2017},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.798516Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:15c74c8bba07cd5a0baa21298715e7d4bbd62a77c435fd53a9bbdb6569859616","observation_id":"27df7659-188b-4225-b997-88420f91e738","resolution":{"observed_at":"2026-08-12T13:06:19.230494Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:18.801350Z","title":"Harnessing artificial intelligence to infer novel spatial biomarkers for the diagnosis of eosinophilic esophagitis,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.801350Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:9d9f56e5e301ef952a755bf7a351e025e6674496e7e594ee402fbda94d91b64b","observation_id":"e8fbe872-a6a4-4c1d-8fe1-e4cd04ea8021","resolution":{"observed_at":"2026-08-12T13:06:18.801350Z","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":"2026-08-12T13:06:18.804000Z","title":"Translational ai and deep learning in diagnostic pathology,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.804000Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:59a18ae81174801ce7cd4e020f1cd7be4f980eb1b1ad6c3a7e2e24d29392dc31","observation_id":"c01126ae-313b-47c5-8c50-34b6f2d663d5","resolution":{"observed_at":"2026-08-12T13:06:18.804000Z","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":"2026-08-12T13:06:18.807025Z","title":"Artificial intelligence and digital pathology: challenges and opportunities,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.807025Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:8d71ce28ad0c924deffbc162f5fd07914a198031c56528877ac44521da795b3b","observation_id":"d41e4b52-4d7b-4594-9351-42a2413cfd73","resolution":{"observed_at":"2026-08-12T13:06:18.807025Z","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":"2026-08-12T13:06:19.208078Z","title":"Pathologygan: Learning deep representations of cancer tissue,","venue":null,"work_id":"24de9b71-8d6b-41d5-b724-ea04bf1c0a27","year":2020},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.809824Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:5683c2d5ee7ff3f6c4c66a4336214c5e359fc015b0ff5e8ec8a849104e9af955","observation_id":"b74b4839-6ef7-4a48-b315-7674da49045a","resolution":{"observed_at":"2026-08-12T13:06:19.211256Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.199005Z","title":"Between generating noise and generating images: Noise in the correct frequency improves the quality of synthetic histopathology images for digital pathology,","venue":null,"work_id":"f8eb4769-5e73-4281-84ae-0cfbf724ca27","year":2023},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.812696Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:16c72ca013ad7c2ec2cf4a94e8655a1756ad40a93ae13b385c60c3eb349f7605","observation_id":"a7b12055-d5ad-4a4e-9879-998d9160d83f","resolution":{"observed_at":"2026-08-12T13:06:19.202395Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.190319Z","title":"Generative adversarial networks for pre-training of medical image segmentation networks,","venue":null,"work_id":"7befe520-a17c-45a9-929f-9a4792de0036","year":2020},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.815420Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:f233bdbdfd1a196385ef956b44b8f6299082716a3ecea2e38d18fd6f9caf457e","observation_id":"d6766204-fbd7-4178-b246-e31db7fdec99","resolution":{"observed_at":"2026-08-12T13:06:19.193435Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1709.01872","last_updated":"2018-01-08T20:32:51Z","snapshot_observed_at":"2026-07-06T05:58:21.950734Z","submitted_at":"2017-09-06T16:07:30Z","title":"Synthetic Medical Images from Dual Generative Adversarial Networks","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1709.01872","snapshot_observed_at":"2026-08-12T13:06:18.818493Z","title":"Synthetic medical images from dual generative adversarial networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.818493Z"},"links":{"cited_paper":"/paper/1709.01872","citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:ca3ae9bcf09d0ffb2c4ffb57bfe716e21f7ce38f70786f9da2555ed7f3715261","observation_id":"f6eeffb3-0112-4dc2-bed6-7854185a90b8","resolution":{"observed_at":"2026-08-12T13:06:18.818493Z","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":"2026-08-12T13:06:19.181372Z","title":"High resolution histopathology image generation and segmentation through adversarial training,","venue":null,"work_id":"e0528c13-4b26-40d6-bcea-a4daf1cab640","year":2022},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.821668Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:e265afe0f8aa72bcc98e8b555f53d1b1003014946e535108c1d79e30b82fe800","observation_id":"aca746b4-cb10-4575-84e2-27935f0e5de9","resolution":{"observed_at":"2026-08-12T13:06:19.184616Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:18.824314Z","title":"Generative adversarial networks,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.824314Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:dd2c00c1ff9193a95a16f872a2986061bd32bbceaf012708e3a8e7c7e69dc4dc","observation_id":"90a697a7-1910-4d46-855d-472d77ff7e74","resolution":{"observed_at":"2026-08-12T13:06:18.824314Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1511.06434","last_updated":"2016-01-07T23:09:39Z","snapshot_observed_at":"2026-08-12T18:43:14.431633Z","submitted_at":"2015-11-19T22:50:32Z","title":"Unsupervised Representation Learning with Deep Convolutional Generative Adversarial Networks","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1511.06434","snapshot_observed_at":"2026-08-12T13:06:18.827222Z","title":"Unsupervised representation learning with deep convolutional generative adversarial networks,","venue":null,"work_id":null,"year":2015},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.827222Z"},"links":{"cited_paper":"/paper/1511.06434","citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:1d9629e368f21d2a82cb2d0093020f5208db4a22329d76857f7b62708306e65f","observation_id":"4dfa4723-afbd-4b10-844c-3c3cff9185d8","resolution":{"observed_at":"2026-08-12T13:06:18.827222Z","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":"2026-08-12T13:06:19.167602Z","title":"Depas: De-novo pathology semantic masks using a generative model,","venue":null,"work_id":"bc531aca-b894-4201-8a96-c664dde2455a","year":2023},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.830301Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:59d440fea01d128aa5e2d07a7c08498a58b11e474aff635319f791497303aa0c","observation_id":"c8cc97db-e112-4784-9347-e40a87f78360","resolution":{"observed_at":"2026-08-12T13:06:19.170981Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.158955Z","title":"Review the cancer genome atlas (tcga): an immeasurable source of knowledge,","venue":null,"work_id":"5bcd98bc-45d3-4677-9ba3-80f700b75a05","year":2015},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.833200Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:cd05b87e5571ec9b791cec2718c96020e648554b8c2bb232ac67e0acbef1bf35","observation_id":"d93ed2b7-deaf-4f5a-a5b7-6908120dbf32","resolution":{"observed_at":"2026-08-12T13:06:19.162061Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.150129Z","title":"Pd-l1 expression in human cancers and its association with clinical outcomes,","venue":null,"work_id":"9dd517ab-fe5a-4cc2-914e-be6adc512fce","year":2016},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.836066Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:2dc02fdee2bb3873a18441b865095839148bd3d2ff57e71ff70e0f6f917c69f0","observation_id":"a0288ebd-0e9c-48f9-974c-1f8799dfb255","resolution":{"observed_at":"2026-08-12T13:06:19.153386Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:18.838848Z","title":"Gans trained by a two time-scale update rule converge to a local nash equilibrium,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.838848Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:01af79d52189c726da6fdd335c2c034a8d1aaf5f4e6cbb940e4ea36839aea2ab","observation_id":"e4a861b2-b789-4475-ac5d-465c27d9823a","resolution":{"observed_at":"2026-08-12T13:06:18.838848Z","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":"2026-08-12T13:06:18.841861Z","title":"Evaluating kolmogorov’s distribution,","venue":null,"work_id":null,"year":2003},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.841861Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:c6762078d4703fdca4ad5b1cd5de8a3dc6068287db0c1c5521cf234aa753035d","observation_id":"039f4169-07d6-4bc1-98d2-89133549f842","resolution":{"observed_at":"2026-08-12T13:06:18.841861Z","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":"2026-08-12T13:06:18.844719Z","title":null,"venue":null,"work_id":null,"year":1997},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.844719Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:b3bd13b275bb2ae52e9b8c24a1deaf86a3fe7496df3c7d52f25c8cdb4c9a00a5","observation_id":"28afef3e-5a57-4184-b58a-ed4b9377409b","resolution":{"observed_at":"2026-08-12T13:06:18.844719Z","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":"2026-08-12T13:06:18.850239Z","title":"Unpaired image-to-image translation using cycle-consistent adversarial networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.850239Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:e2253dbf88596a46506e18b35ff63f726d0936428e05b1928ba0ba8a7ee900e4","observation_id":"9eb7f419-e527-4756-b9cf-5efa8c0d9f34","resolution":{"observed_at":"2026-08-12T13:06:18.850239Z","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":"2026-08-12T13:06:19.116879Z","title":"Labelme: Image polygonal annotation with python","venue":null,"work_id":"efb93ecc-f3fc-42a9-9d7a-fe9d7be48b55","year":null},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.853064Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:1a15899e6e1bedc203a7103c0493e5916fe5f58a1822c5eccfad00d3bfb1a902","observation_id":"e50f9cb7-e122-4e0d-8618-4ad677cef8e3","resolution":{"observed_at":"2026-08-12T13:06:19.119985Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.108503Z","title":"Medibang inc,","venue":null,"work_id":"ec771dac-c3cc-4104-add4-ee5fe9766425","year":null},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.856058Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:6294daffc0a3422d3532db91ce35dd44b57207276333cbef562826b3fe2d091d","observation_id":"71d4a321-78a3-487e-a77e-f1eb45a39c48","resolution":{"observed_at":"2026-08-12T13:06:19.111436Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.099441Z","title":"Samsung penup","venue":null,"work_id":"2671e3f1-5fb0-4d38-9004-eefc1963ec9a","year":null},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.858978Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:0b264531a007b5340fdfb9ef7aca1f298ac27fa319d52a5c05f6c1f5efed35ac","observation_id":"143e9d7d-9742-4595-839c-cf2b5801d85b","resolution":{"observed_at":"2026-08-12T13:06:19.102770Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}},{"citation":{"cited_paper":{"arxiv_id":"1411.1784","last_updated":"2014-11-06T22:33:22Z","snapshot_observed_at":"2026-08-12T18:51:00.108451Z","submitted_at":"2014-11-06T22:33:22Z","title":"Conditional Generative Adversarial Nets","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1411.1784","snapshot_observed_at":"2026-08-12T13:06:18.861816Z","title":"Conditional generative adversarial nets,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.861816Z"},"links":{"cited_paper":"/paper/1411.1784","citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:d8f9539b1e938451b749904dce4e76439ee4f7aeccdbb7e30ad3822fead19f5b","observation_id":"a3a94be3-3061-46bc-9aa7-7b9709981a52","resolution":{"observed_at":"2026-08-12T13:06:18.861816Z","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":"2026-08-12T13:06:19.090764Z","title":"Medical image computing and computer-assisted intervention–miccai 2015,","venue":null,"work_id":"f6a46136-f5e4-475d-8eee-7c46d3599a42","year":2015},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.865152Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:2cea6fcaffd8d0e01cf5bd9d4340c10f94120c91abad73d36168a0682da710f1","observation_id":"f7b65e14-f586-4949-870f-c30285e9a92c","resolution":{"observed_at":"2026-08-12T13:06:19.093835Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:18.867994Z","title":"Image-to-image translation with conditional adversarial networks,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.867994Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:a2cc85db72c1018c4ce4fe1d582e1aba8d12d45568e56d757a8e6705aa250007","observation_id":"1c0b2878-b364-4fe9-93d6-c59458dc59e0","resolution":{"observed_at":"2026-08-12T13:06:18.867994Z","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":"2026-08-12T13:06:18.873592Z","title":"Deep residual learning for image recognition,","venue":null,"work_id":null,"year":2016},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.873592Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:758bc4f5276b992c433a3ab71fc74d605cabee47e5d9d544af8db4b3b2b74685","observation_id":"52343207-8618-406a-ace3-f9a345548e45","resolution":{"observed_at":"2026-08-12T13:06:18.873592Z","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":"2026-08-12T13:06:19.071590Z","title":"Pytorch: An imperative style, high-performance deep learning library,","venue":null,"work_id":"935eaafb-49c8-4e32-8a4f-2ce82032efe7","year":2019},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.876397Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:05c12b54b82d3965c8544ef1915d0b805446746233001b295a0075143ebaa432","observation_id":"5a5ebf5d-b36f-4543-8f56-d4e3985ea507","resolution":{"observed_at":"2026-08-12T13:06:19.074717Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:18.879155Z","title":"Adam: A method for stochastic optimization,","venue":null,"work_id":null,"year":2014},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.879155Z"},"links":{"cited_paper":"/paper/1412.6980","citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:a99b9d7bc860fe431de87fbddbfabfc5e792cc3276da585150f67afada40607a","observation_id":"f904c01d-5a4a-4a47-a072-c6c5e6ff3160","resolution":{"observed_at":"2026-08-12T13:06:18.879155Z","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":"2026-08-12T13:06:18.882020Z","title":"High-resolution image synthesis and semantic manipulation with conditional gans,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.882020Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:49941b11aefb51c065e58ba125bcf542ebdd349e91481f62c96fdaed74169632","observation_id":"06b361ca-9194-4b17-99eb-7a6123ba3d25","resolution":{"observed_at":"2026-08-12T13:06:18.882020Z","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":"2026-08-12T13:06:19.062838Z","title":"Synthesis of diagnostic quality cancer pathology images by generative adversarial networks,","venue":null,"work_id":"2c34b825-55dd-4f98-ad07-7ef19baf8458","year":2020},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.884733Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:eed350099ff130d45f81128e323b09683ba23d34fb10bb5a25f29ec2e27f69a3","observation_id":"cc098720-735d-4c72-9f3d-3e5aa7b6e4d5","resolution":{"observed_at":"2026-08-12T13:06:19.066137Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+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":"2026-08-12T13:06:19.052836Z","title":"A morphology focused diffusion probabilistic model for synthesis of histopathology images,","venue":null,"work_id":"587787d3-4e32-4e91-ab57-d1317bfd516a","year":2023},"citing_paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-12T13:06:18.887708Z"},"links":{"citing_paper":"/paper/2411.16515"},"observation_digest":"sha256:c232778f88ec1816a66f15d4cdbd85157e1c6bbe878ce35debe7b9d0bd79642e","observation_id":"61f01976-f287-4978-a31a-23c4aac3ac90","resolution":{"observed_at":"2026-08-12T13:06:19.057026Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-12T06:34:41.77262+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2411.16515","last_updated":"2024-12-03T07:48:06Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-12T16:45:48.511925Z","submitted_at":"2024-11-25T15:57:19Z","title":"PriorPath: Coarse-To-Fine Approach for Controlled De-Novo Pathology Semantic Masks Generation"},"reference_resolution":{"displayed":43,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":18,"verified_exact":1,"verified_fuzzy":24},"total_outbound_references":43},"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":"2026-08-12T06:34:41.77262+00:00","source":"crossref"},{"observed_at":"2026-08-12T06:34:36.333875+00:00","source":"retraction_watch"}],"thesis":"As of 13 August 2026, this Paper Citation Record lists 43 of 43 outbound references and 1 inbound Pith citation observation for arXiv:2411.16515."}