{"as_of":"2026-08-10T01:12:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:7d1ddad7035373fd66288e4c7bd430ea4d1bef7b60c024045fa793996b36ff14","coverage":[{"denominator":40,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":40,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-09T12:02:59.108624Z","state":"measured"},{"denominator":41,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":41,"source":"scholarly_work_events, retraction_status_cache","source_observed_at":"2026-08-09T06:31:02.800959+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":"2026-08-06T14:36:42.073968Z","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":"2026-08-06T14:36:42.264224Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"cited_work":{"arxiv_id":"2502.02489","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.02489","snapshot_observed_at":"2026-08-06T14:36:42.264224Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","venue":"cs.CV","work_id":"6fa5dc7e-11ae-49d4-b872-8a095531f7cc","year":2025},"citing_paper":{"arxiv_id":"2507.18424","last_updated":"2025-07-24T14:01:02Z","snapshot_observed_at":"2026-08-08T09:26:54.372507Z","submitted_at":"2025-07-24T14:01:02Z","title":"Self-Supervised Ultrasound-Video Segmentation with Feature Prediction and 3D Localised Loss","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-06T14:36:42.073968Z"},"links":{"cited_paper":"/paper/2502.02489","citing_paper":"/paper/2507.18424"},"observation_digest":"sha256:e9b7922ca7155c791950cf2b60301fb3a04909973df268dcd67f9f3d5c804ecc","observation_id":"28ec5300-0345-4121-a455-9f5f35237429","resolution":{"observed_at":"2026-08-06T14:36:42.271835Z","resolver_source":"local_arxiv","status":"metadata_mismatch"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"links":{"evidence":"/evidence","html":"/paper/2502.02489/citation-record","integrity":"/paper/2502.02489/integrity","json":"/paper/2502.02489/citation-record.json","paper":"/paper/2502.02489"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-09T12:02:59.517752Z","title":"Application of ultrasound in medicine,","venue":null,"work_id":"492a727e-5fdc-462e-816d-396211b2f3bc","year":2011},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:58.978165Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:a54361b852ea0bf3ac7cef3ea157e16d2b41173b4a2dc0f12ed635e3b8966521","observation_id":"d648311b-352d-4d89-b03d-15ebec608724","resolution":{"observed_at":"2026-08-09T12:02:59.521240Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.507690Z","title":"Machine learning for medical ultrasound: status, methods, and future opportunities,","venue":null,"work_id":"6a781555-8752-4f1a-a656-81968c911905","year":2018},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:58.982809Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:1cdab1d7219159227f09337fcbf21861375434d85e6dc0ca8009cecc99a873d9","observation_id":"809249f7-58ae-4a69-9141-3c93aa00836d","resolution":{"observed_at":"2026-08-09T12:02:59.511281Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.498174Z","title":"A hybrid enhanced attention transformer network for medical ultrasound image segmentation,","venue":null,"work_id":"c94ad6c4-eb7c-44fe-94f9-8ce155d03392","year":2023},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:58.986553Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:a2a3aeeb64ad7315bed4e813f5d5d887d14ba8ea3f61568e4671f97e72fcf6d1","observation_id":"44336597-bdb6-487d-a8ed-4a0ac39d8dcd","resolution":{"observed_at":"2026-08-09T12:02:59.501648Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.488194Z","title":"HAU-Net: Hybrid CNN-transformer for breast ultra- sound image segmentation,","venue":null,"work_id":"c165db00-bbc9-4495-b938-84da7cad587f","year":2024},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:58.990203Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:d3ec7e31948fc25724792d4e559906cc82a03710f104e686861d8a20c39cd9a7","observation_id":"ecf20477-51a4-49d7-b5d8-d7ed23f8ec18","resolution":{"observed_at":"2026-08-09T12:02:59.491738Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.478532Z","title":"Cross-Image Dependency Modeling for Breast Ultrasound Segmentation,","venue":null,"work_id":"23cd79df-b712-4f8b-9841-9cf60ebb1c68","year":2023},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:58.994313Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:4efb21a8f92dc235ace3af910d9838148411997542e318bb965605844cf96245","observation_id":"53cc0692-3c32-4f60-8dee-1d1c396cdf37","resolution":{"observed_at":"2026-08-09T12:02:59.481970Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.468780Z","title":"Unified semantic model for medical image segmenta- tion,","venue":null,"work_id":"0c723fef-b609-4a41-a3e3-531380cac41e","year":2024},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:58.997917Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:329bad9efb3da5c26ca22b510b78aa7cc9f3db17926a16587296ee26f2540b72","observation_id":"762577c2-f949-4c60-b647-37540fef9b6e","resolution":{"observed_at":"2026-08-09T12:02:59.472402Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.459618Z","title":"Self-supervised learning is more robust to dataset imbalance,","venue":null,"work_id":"2e26fe30-fd79-4732-b8ba-5deb056752c9","year":2022},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.001755Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:32eef3565db7769b2cf042bd2eb58c861182e7b774c06d174871b972bcbb3884","observation_id":"67e6c856-f454-4088-baf1-1abbe34599d3","resolution":{"observed_at":"2026-08-09T12:02:59.462950Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.450428Z","title":"Toward Generalizability in the Deployment of Artifi- cial Intelligence in Radiology: Role of Computation Stress Testing to Overcome Underspecification,","venue":null,"work_id":"533321f6-f772-494c-8534-10248c5c0f5f","year":2021},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.005128Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:15f5c680e753ad6630f95a40400a033c4649d634a39bc48aa04ea6ad3c9e7bb9","observation_id":"6340f882-1830-4e7c-988b-1cad7cafe66c","resolution":{"observed_at":"2026-08-09T12:02:59.453830Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.440757Z","title":"Big Self-Supervised Models Advance Medical Image Classification,","venue":null,"work_id":"69b9ab5f-334f-44f4-9644-62020f11811e","year":2021},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.008332Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:8c0eaaf119d4f893fe9017d491a40c73b97fd5ed68e69822ebb14eb71e14cbc4","observation_id":"e805bff7-fc51-405a-b2b5-76894f26b020","resolution":{"observed_at":"2026-08-09T12:02:59.444228Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.431399Z","title":"Self-supervised learning for medical image analysis using image context restoration,","venue":null,"work_id":"bb226792-bb1c-4cf1-94cb-e01220513ffe","year":2019},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.011360Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:6b320c77fedc45d7427d0e0a1345152d1d86764de28fb88f5cb60dd82a01506a","observation_id":"735752cd-4e69-4de8-aaf0-40454d540e19","resolution":{"observed_at":"2026-08-09T12:02:59.434638Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.422026Z","title":"SSL-CPCD: Self-supervised learning with composite pretext-class discrimination for improved generalisability in endoscopic image analysis,","venue":null,"work_id":"f8752ef3-a45b-4f6b-9bd7-883b06a87619","year":2024},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.014495Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:70d45d7b3c453c9ba08bf36b7ec0d41282899a9ed07cfa11d860aea9df784541","observation_id":"2d3cd481-6d0b-4dc5-ad39-71e9098508c8","resolution":{"observed_at":"2026-08-09T12:02:59.425477Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.409909Z","title":"VanBerlo et al., “A survey of the impact of self-supervised pretraining 12 Fig","venue":null,"work_id":"dcb8a1bd-fffd-482f-80df-3e9f5164fb41","year":2024},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.017555Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:901bc7807e8304209036f9146f85afe15f3cd5f900cd280833e66e574ec7db08","observation_id":"9ef9b677-b240-48c0-8319-d1fc58111186","resolution":{"observed_at":"2026-08-09T12:02:59.414635Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.400318Z","title":"Self-Supervised Learning to More Efficiently Generate Segmentation Masks for Wrist Ultrasound,","venue":null,"work_id":"e9cfad46-0285-4d9a-b43c-b96f2d3e5b65","year":2023},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.020740Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:dac7530658b31af7a9e40fb864bdd1a3c9de4bb7eed1d504c74522bfa9885edb","observation_id":"1a1d8a0d-5f0a-44ef-a2bb-5c7c19544144","resolution":{"observed_at":"2026-08-09T12:02:59.403783Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.390932Z","title":"Thyroid ultrasound diagnosis improvement via multi- view self-supervised learning and two-stage pre-training,","venue":null,"work_id":"7864ebb0-27d1-41b1-b786-d0cd8ac8ffeb","year":2024},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.023714Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:f9b86ba013533e907e56214a5191b1d3e6d59ba667fc82ca693815ca068d5e2e","observation_id":"0a14856d-2935-433c-909b-208f9d6c28fb","resolution":{"observed_at":"2026-08-09T12:02:59.394459Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.381627Z","title":"Self-Supervised Learning: Generative or Contrastive,","venue":null,"work_id":"0924aebb-e64a-42b1-ba71-e4ef46cd2fae","year":2023},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.026732Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:a79083172b129c188ae98a036cca32d57a7923ff6a7dada61426f2707289a335","observation_id":"21e00ae5-ea36-423d-9441-d2f0b899433c","resolution":{"observed_at":"2026-08-09T12:02:59.385064Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.372351Z","title":"Self-Supervised Learning of Pretext- Invariant Representations,","venue":null,"work_id":"08b92561-67b4-4205-bcf4-c2b022218a66","year":2020},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.029978Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:7ee36832a6cf7a66f8f18a74fa510cf948c390d81f9d3dd56e10319c09230e55","observation_id":"87274d0a-baf3-4052-85a7-b354dc1342ea","resolution":{"observed_at":"2026-08-09T12:02:59.375534Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.362973Z","title":"Unsupervised Learning of Visual Represen- tations by Solving Jigsaw Puzzles,","venue":null,"work_id":"2e7c0c05-93a7-41df-bfa2-08ba3eb464ab","year":2016},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.032998Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:518dd83325bd652f5f22951a9f90bc11369dbc2aa0129bd5bf18772fccfe94ab","observation_id":"e79c7424-6ac1-4dc8-9cdb-456504bb8df9","resolution":{"observed_at":"2026-08-09T12:02:59.366223Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.353816Z","title":"A simple framework for contrastive learning of visual representations","venue":null,"work_id":"27ec72f9-c8e4-48cf-ac21-f14260403b35","year":2020},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.035720Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:8478e20cc45b74b87b9fb0c562850d0f9302c3ea61f866ec62a56c04243fd72d","observation_id":"a36350d8-b29e-40b8-b266-517f62b7d8df","resolution":{"observed_at":"2026-08-09T12:02:59.357179Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.344285Z","title":"U-Net: Convolutional Networks for Biomedical Image Segmentation,","venue":null,"work_id":"46c8215e-5958-43d3-9306-3a3ffb8ed590","year":2015},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.038797Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:3371126aa8f30bd295cd79d9eb7c7d8435ca1b6e064d503cee7b23587a1f8825","observation_id":"0782933a-18d3-4c0f-95d9-d54121c936f2","resolution":{"observed_at":"2026-08-09T12:02:59.347586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.334678Z","title":"ESTAN: Enhanced Small Tumor-Aware Network for Breast Ultrasound Image Segmentation,","venue":null,"work_id":"61a545a1-4bef-4180-8c40-dc09194ed935","year":2022},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.042080Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:f0d62be6eec43aa87e346f4c26ec87e40b13418279016900489f91a235a8cf5e","observation_id":"2735f2e3-cc07-4d7f-90f0-96db69718402","resolution":{"observed_at":"2026-08-09T12:02:59.338089Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.325003Z","title":"Ultrasound spine image segmentation using multi-scale feature fusion skip-inception U-Net (SIU-Net),","venue":null,"work_id":"0f96363d-30c3-4337-8dc2-5d4c7965beb7","year":2022},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.045505Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:d5c99a3ed5c8fa16a5a97e9cd47a345d1eaf39f2c23a16c6d0c01d4f7b12e153","observation_id":"e8e4be40-1ca7-482a-996a-d1de81e38c44","resolution":{"observed_at":"2026-08-09T12:02:59.328586Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.315498Z","title":"Deep Learning for Carotid Plaque Segmentation using a Dilated U-Net Architecture,","venue":null,"work_id":"10e95e5d-4380-4123-a635-5d62b105e03f","year":2020},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.048724Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:8a9052abf1c95fb648e0cf1d6549110b204bc10689d4849b213617efc6c0444a","observation_id":"a166bc3f-3caa-4ef4-b5a4-895d396f031d","resolution":{"observed_at":"2026-08-09T12:02:59.319250Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.306328Z","title":"Dilated Squeeze-and-Excitation U-Net for Fetal Ultrasound Image Segmentation,","venue":null,"work_id":"a4b6c036-05ef-4285-bd71-8893c3524914","year":2020},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.051818Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:e783ffba354cbbc87294cdc706e8a7985b937e4596a9c2c24ca3cdb8ae0a812e","observation_id":"ce1ed7fd-a7d1-4122-9a3b-7b8e946760db","resolution":{"observed_at":"2026-08-09T12:02:59.309588Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.297174Z","title":"Unsupervised representation learning by predicting image rotations,","venue":null,"work_id":"0422cd24-3587-40c8-8802-7938cde77f26","year":2018},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.055513Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:16504e32996738dc91e44adf51cb05f00e2a20cc79240bf78d74e95af10672c1","observation_id":"37d89cdc-76d7-41fd-b2d4-40aa670177dd","resolution":{"observed_at":"2026-08-09T12:02:59.300619Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.287231Z","title":"Identification method of thyroid nodule ultrasonography based on self-supervised learning dual-branch attention learning frame- work,","venue":null,"work_id":"b6fbc888-432a-4002-95a7-c17b080b38ea","year":2024},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.059317Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:e70733797be9bfb48b90d74eff9a27a1a51b6afd008118f96beec700899dc052","observation_id":"52bfbbc7-3daf-43b9-91d6-47e8d738ce4e","resolution":{"observed_at":"2026-08-09T12:02:59.290828Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.277692Z","title":"Twin self-supervision based semi-supervised learning (TS-SSL): Retinal anomaly classification in SD-OCT images,","venue":null,"work_id":"1efc2f4d-b44a-4dad-a486-647ce79cdd0f","year":2021},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.062932Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:9796a64b3d08dfbd959560b7c86b2f038f392450006af50e4515d541212822c1","observation_id":"e6c10f30-6536-4da6-947f-acfc633b56c4","resolution":{"observed_at":"2026-08-09T12:02:59.281059Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.268204Z","title":"SimMIM: a Simple Framework for Masked Image Modeling,","venue":null,"work_id":"55b9ba0b-b357-40bf-9c8e-eb80545fe98f","year":2022},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.066364Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:e4c386843c7bb3b3711b751a64efc95bb4be6178824a977f4d3401cdad175776","observation_id":"5ec7bebc-19ef-439b-abbc-90561525b00c","resolution":{"observed_at":"2026-08-09T12:02:59.271503Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.258795Z","title":"Momentum Contrast for Unsupervised Visual Represen- tation Learning,","venue":null,"work_id":"fff51cd7-75fe-4e0e-8afc-3be7253c4c05","year":2020},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.069601Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:31acba2b5a2be2e2c80e2facebba6c65c3f5e189625d8ebfba06bf4fde9b3935","observation_id":"f845cca3-1088-4921-96e1-cd243617b64e","resolution":{"observed_at":"2026-08-09T12:02:59.262144Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.249384Z","title":"Bootstrap your own latent-a new approach to self- supervised learning,","venue":null,"work_id":"501934aa-87a9-4676-b4d0-e63f03a28ae3","year":2020},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.073163Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:c5df8aa13855d318cdca193094cd12a0c0b3d2fed040cd9164a7f06e73637420","observation_id":"20ceed1b-f269-4623-8735-9fa37da9fa6a","resolution":{"observed_at":"2026-08-09T12:02:59.252765Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.239699Z","title":"Siamese neural networks for one-shot image recogni- tion,","venue":null,"work_id":"19060086-d44f-4fb6-a83c-c0dd12d27e6d","year":2015},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.076290Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:0b859622900440a1382ed2ecfbd2ae13efddc8b8d75c1c9ff21fc58f7ac5a3de","observation_id":"f31eb849-a867-4b01-a2c0-cf819ed33541","resolution":{"observed_at":"2026-08-09T12:02:59.243405Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.230090Z","title":"Prototypical networks for few-shot learning,","venue":null,"work_id":"6b9ff5ee-31d7-4ee5-ad1c-2ca94680405d","year":2017},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.079600Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:8a2a2e4f727fb6c627cd868706be77f012cf88643d2c6510247fc6370f290174","observation_id":"2a34a4ff-682e-49e6-be9e-c501aee449ac","resolution":{"observed_at":"2026-08-09T12:02:59.233563Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.220277Z","title":"Learning to Compare: Relation Network for Few-Shot Learning,","venue":null,"work_id":"22949ddd-2823-4492-806b-20a2e9b75ae6","year":2018},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.082784Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:8c96c7b0a15f10763f7e2bff225fc60656978590a4784d83c9cd2c6baad37923","observation_id":"0cb15115-5da0-43aa-b573-0132954616c6","resolution":{"observed_at":"2026-08-09T12:02:59.223852Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.210056Z","title":"ImageNet: A large-scale hierarchical image database,","venue":null,"work_id":"10a126e7-a1fb-4402-b675-7dc2edf94f35","year":2009},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.086055Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:0d0ee646ce7bc9b517eadebf3a398b33dc4fa93d3f7899cf65e48c035d31303d","observation_id":"69a2063f-0500-41c5-92a1-563c52be814b","resolution":{"observed_at":"2026-08-09T12:02:59.213702Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.200117Z","title":"Deep Residual Learning for Image Recognition,","venue":null,"work_id":"c09eee4b-eb5e-4d43-8e54-2e8c42e9217c","year":2016},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.089137Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:9b68490c70984c31c77432f76c17252c56b3ba0c4e56fc7c796372700c66319e","observation_id":"0af6cb1c-e855-42e5-af1d-8a8382c1092e","resolution":{"observed_at":"2026-08-09T12:02:59.203775Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.189418Z","title":"Unsupervised Feature Learning via Non-parametric Instance Discrimination,","venue":null,"work_id":"ff377583-9618-4549-b683-cd87b1fd6a24","year":2018},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.092361Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:e9f2efe2d8beabf3dcddefe4d6ae83f766f3a9986ae5194a9eefd14a96ffd65e","observation_id":"2d377dea-ba71-4f3d-bca0-00a550b2afb6","resolution":{"observed_at":"2026-08-09T12:02:59.193427Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.178101Z","title":"Self-Supervised Learning for Accurate Liver View Classification in Ultrasound Images with Minimal Labeled Data,","venue":null,"work_id":"2b778869-4f0f-47bc-965e-08cc47b008e5","year":2023},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.095577Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:51a4537c7242900e2ef5a941f0195c823bf0759f3b18bbfc4a61cee16be59328","observation_id":"f08158fd-5269-4e5f-bf4b-3d729cc0217a","resolution":{"observed_at":"2026-08-09T12:02:59.181699Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.167948Z","title":"Dataset of breast ultrasound images,","venue":null,"work_id":"d8acf0a9-0ec0-404e-a729-6f110a537152","year":2020},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.098964Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:518d6adf4b79f9c6fc0d8436ae6ca301ab3695d0d801b144089792ce40c817db","observation_id":"60c2abfa-c8c7-4cc7-9d92-8367c1ad7e8a","resolution":{"observed_at":"2026-08-09T12:02:59.171545Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.156752Z","title":"Curated benchmark dataset for ultrasound based breast lesion analysis,","venue":null,"work_id":"1a6042f8-6326-4e39-8872-df62131298ac","year":2024},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.102073Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:003111e50ac269c29b8938a07fe824ff0a5a24bec2f41d06bcb973c1582e6b32","observation_id":"690b2981-a421-40fc-bcd4-c476d1bca732","resolution":{"observed_at":"2026-08-09T12:02:59.160443Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.146226Z","title":"Automated breast ultrasound lesions detection using convolutional neural networks,","venue":null,"work_id":"4affa781-c7cd-4316-a9dc-8c452d40236f","year":2017},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.105459Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:bf8aa3318472ad038f35a33053aab756df3ac367cec23fee8f03ec8c62197267","observation_id":"01aea7d5-846a-4a8c-a64b-4c72ad9b1de3","resolution":{"observed_at":"2026-08-09T12:02:59.149930Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+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":"2026-08-09T12:02:59.133465Z","title":"Road extraction by deep Residual U-Net,","venue":null,"work_id":"3dbc0997-88b9-4feb-94ff-061d8b9b597c","year":2018},"citing_paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation","version":1},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-09T12:02:59.108624Z"},"links":{"citing_paper":"/paper/2502.02489"},"observation_digest":"sha256:6ff5a14fe5171058d6ea0abb3adf627cbea8acfdd4ba9afc546cbb8397e8dc8f","observation_id":"d15aff1e-0b08-45a5-9388-1170ffe539bc","resolution":{"observed_at":"2026-08-09T12:02:59.138964Z","resolver_source":"raw_fallback","status":"verified_fuzzy"},"standing_notice":{"events":[],"observation":"No event found in the named queried sources as of 2026-08-09T06:31:02.800959+00:00.","reason":null,"source_receipts":[{"observed_at":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"state":"measured"}}],"paper":{"arxiv_id":"2502.02489","last_updated":"2025-02-04T17:06:41Z","latest_version":1,"primary_category":"cs.CV","snapshot_observed_at":"2026-08-09T12:09:09.110358Z","submitted_at":"2025-02-04T17:06:41Z","title":"A Self-Supervised Framework for Improved Generalisability in Ultrasound B-mode Image Segmentation"},"reference_resolution":{"displayed":40,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":0,"verified_exact":0,"verified_fuzzy":40},"total_outbound_references":40},"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":"2026-08-09T06:31:02.800959+00:00","source":"crossref"},{"observed_at":"2026-08-09T06:30:57.326959+00:00","source":"retraction_watch"}],"thesis":"As of 10 August 2026, this Paper Citation Record lists 40 of 40 outbound references and 1 inbound Pith citation observation for arXiv:2502.02489."}