{"as_of":"2026-08-10T03:07:00Z","caps":{"database_statements":6,"inbound":100,"outbound":100},"context_digest":"sha256:9a191fd42839db6fb6e70a2750541ee315304ac65ed0af72bcc2c910405cbd19","coverage":[{"denominator":58,"lane":"reference_resolution","note":"Typed states for the displayed outbound observations.","records_observed":58,"source":"paper_references, paper_reference_links","source_observed_at":"2026-08-08T20:10:52.426326Z","state":"measured"},{"denominator":59,"lane":"standing_notices","note":"One-hop event checks from named stored sources.","records_observed":59,"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":"2026-08-07T00:14:13.598291Z","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":"2026-08-07T00:14:14.037644Z","state":"measured"}],"external_citation_measurements":[],"inbound":[{"citation":{"cited_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"cited_work":{"arxiv_id":"2502.05142","doi":null,"metadata_source":"pith","pith_arxiv_id":"2502.05142","snapshot_observed_at":"2026-08-07T00:14:14.037644Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","venue":"eess.IV","work_id":"7ca1ea8b-ed7c-46bb-b085-0e0142aace8c","year":2025},"citing_paper":{"arxiv_id":"2506.19055","last_updated":"2025-06-17T20:17:07Z","snapshot_observed_at":"2026-08-09T13:19:16.620393Z","submitted_at":"2025-06-17T20:17:07Z","title":"Xray2Xray: World Model from Chest X-rays with Volumetric Context","version":1},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-07T00:14:13.598291Z"},"links":{"cited_paper":"/paper/2502.05142","citing_paper":"/paper/2506.19055"},"observation_digest":"sha256:5e4012aa5157fb8059a40f8b175ad9c49390c2452119c558d4c125f76c919deb","observation_id":"58f84c5e-960e-4f2f-b3b0-fe41f229174c","resolution":{"observed_at":"2026-08-07T00:14:14.108229Z","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/2502.05142/citation-record","integrity":"/paper/2502.05142/integrity","json":"/paper/2502.05142/citation-record.json","paper":"/paper/2502.05142"},"outbound":[{"citation":{"cited_paper":null,"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":"raw_reference","pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:10:53.236903Z","title":"Interpretation of plain chest roentgenogram,","venue":null,"work_id":"ca78d9b9-8280-4265-9437-1396301cf341","year":2012},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":1,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.150312Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:8efbd18e760dfcf865f3ef481b7f5851415f6b2930b1badf8b64093a2247a020","observation_id":"2fafe7bb-0bd6-478e-9731-ef0787e5c0bc","resolution":{"observed_at":"2026-08-08T20:10:53.240970Z","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":"2026-08-08T20:10:53.222389Z","title":"Deep learning to estimate cardiovascular risk from chest radiographs,","venue":null,"work_id":"bed82b5c-fb46-436a-975a-1d74bea1f104","year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":2,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.155803Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:c3206f000e543e27af1f1bca2fb78decfa05e3557f8e7321b649d0dd72594d18","observation_id":"dba7adc9-7a62-4984-adae-305876af0bd4","resolution":{"observed_at":"2026-08-08T20:10:53.227245Z","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":"2026-08-08T20:10:53.207170Z","title":"Cardiovascular disease detection from multi-view chest x-rays with bi-mamba,","venue":null,"work_id":"8d33bed2-c754-4fc3-99d9-f0eb7ba33d30","year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":3,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.161097Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:907208ed4f4a40d35ca79b58c15ba3a19e04707cf2d87aa0df9d82d1b3111ebc","observation_id":"fe0fb3c8-61fe-4bcb-837a-58fb51175cf4","resolution":{"observed_at":"2026-08-08T20:10:53.212397Z","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":"2026-08-08T20:10:53.192400Z","title":"Deep learning to estimate lung disease mortality from chest radiographs,","venue":null,"work_id":"959a0c04-83ba-40de-8105-aaddd322c494","year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":4,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.166695Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:944958e0b166011bb5c1f47f86439844eca990b147ec9b75b5e679050f8082f4","observation_id":"e5a470e7-a0c9-454e-92f1-1c721ad25e6e","resolution":{"observed_at":"2026-08-08T20:10:53.197118Z","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":"2026-08-08T20:10:53.177802Z","title":"Opportunistic detection of type 2 diabetes using deep learning from frontal chest radiographs,","venue":null,"work_id":"9c154dd0-f5a0-4bad-b9e7-adb2b2387a25","year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":5,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.171584Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:6a5254ff96ff4216103cd166d70ef08aa8883a3cc2ccd4e61e98fb1baaad622a","observation_id":"55781622-d8f2-4618-b959-4882c240e788","resolution":{"observed_at":"2026-08-08T20:10:53.182610Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:10:52.176480Z","title":"An empirical study of training self- supervised vision transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":6,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.176480Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:0d59b568758284ca29bd397c00950676d32b2846acad09f448620fe160236ba7","observation_id":"419d58cd-042a-4fb3-b2bb-c069ef9c7d42","resolution":{"observed_at":"2026-08-08T20:10:52.176480Z","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":"2026-08-08T20:10:52.181701Z","title":"Masked au- toencoders are scalable vision learners,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":7,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.181701Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:ead9f861f1e23cfd253d4cc472833416984997947982869304f030d45ff5270e","observation_id":"97f37592-b705-4d35-8c27-e9233f170d4a","resolution":{"observed_at":"2026-08-08T20:10:52.181701Z","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":"2026-08-08T20:10:52.186565Z","title":"Emerging properties in self-supervised vision transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":8,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.186565Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:b79f3b58f3493815da10225ba7ed8636217aba4fbfb68e4d55d5d9400786b1e8","observation_id":"73233b6a-0e78-4336-9eaa-63a163503707","resolution":{"observed_at":"2026-08-08T20:10:52.186565Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2111.07832","last_updated":"2022-01-27T09:20:49Z","snapshot_observed_at":"2026-07-06T12:08:39.149450Z","submitted_at":"2021-11-15T15:18:05Z","title":"iBOT: Image BERT Pre-Training with Online Tokenizer","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2111.07832","snapshot_observed_at":"2026-08-08T20:10:52.191222Z","title":"ibot: Image bert pre-training with online tokenizer,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":9,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.191222Z"},"links":{"cited_paper":"/paper/2111.07832","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:5e659df041fd9f15caa35180eb786c35bfd322ad3dddfa2c51eff6916b9ceebf","observation_id":"f7c62d5e-d61c-4595-a24c-797f544662e4","resolution":{"observed_at":"2026-08-08T20:10:52.191222Z","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":"2026-08-08T20:10:52.196995Z","title":"Robust and data-efficient generalization of self-supervised machine learning for diagnostic imaging,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":10,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.196995Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:3611dabaca1498b490ea84d8988127d5320899b73c0a3c964e19de333147bd91","observation_id":"2d7243ce-96b5-4e61-b32d-0c3814230855","resolution":{"observed_at":"2026-08-08T20:10:52.196995Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.05237","last_updated":"2024-05-08T17:33:42Z","snapshot_observed_at":"2026-08-06T01:59:01.455119Z","submitted_at":"2024-05-08T17:33:42Z","title":"EVA-X: A Foundation Model for General Chest X-ray Analysis with Self-supervised Learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.05237","snapshot_observed_at":"2026-08-08T20:10:52.201818Z","title":"Eva-x: A foundation model for general chest x-ray analysis with self- supervised learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":11,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.201818Z"},"links":{"cited_paper":"/paper/2405.05237","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:4efe713ad4194c62b427a0dae3b0342740d6fabbcac98f1dcd240a65bdbfc508","observation_id":"1da463f0-6801-498b-b206-bd60c4ca83ba","resolution":{"observed_at":"2026-08-08T20:10:52.201818Z","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":"2026-08-08T20:10:52.208082Z","title":"Exploring scalable medical image encoders beyond text supervision,","venue":null,"work_id":null,"year":2025},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":12,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.208082Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:9d2860ed11b8d22a6dc3d87756f027cb960add75317c454d4b9cb244ebc62a43","observation_id":"4f67788f-7cce-431a-a901-efd71714828d","resolution":{"observed_at":"2026-08-08T20:10:52.208082Z","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":"2026-08-08T20:10:53.117138Z","title":"Expert-level detection of pathologies from unannotated chest x-ray images via self-supervised learning,","venue":null,"work_id":"83b10c61-c166-495f-804a-5d26e98a5992","year":2022},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":13,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.213801Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:89060d74dd8d09b801f8d108c1c35d7d572c0068a098f0acdb1354db818c0592","observation_id":"ea7e8b21-4fc1-4636-8581-acae80cb2dd7","resolution":{"observed_at":"2026-08-08T20:10:53.121605Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:10:52.218725Z","title":"Mimic-cxr, a de- identified publicly available database of chest radiographs with free-text reports,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":14,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.218725Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:6e8de6d44d1acd8ce18613b403ec2896148e5a98275556c9830aac46b2237619","observation_id":"d33e7add-140d-4847-8b23-2a1f9b772f7b","resolution":{"observed_at":"2026-08-08T20:10:52.218725Z","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":"2026-08-08T20:10:52.223431Z","title":"Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison,","venue":null,"work_id":null,"year":2019},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":15,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.223431Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:93517bd3b75fad59f87254ccf339980ba0ff3ccd8660fbe3f3da7baaff8958a3","observation_id":"9e3c14f5-9122-469d-9471-fbd3260c7c4e","resolution":{"observed_at":"2026-08-08T20:10:52.223431Z","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":"2026-08-08T20:10:52.228041Z","title":"Padchest: A large chest x-ray image dataset with multi-label annotated reports,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":16,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.228041Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:f7742fd7646c7d27b3e793595cf9085b846a7c7fac3112430e6b2e8bffc62342","observation_id":"2c251305-16ee-4f05-8fdc-ebeb4a42f151","resolution":{"observed_at":"2026-08-08T20:10:52.228041Z","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":"2026-08-08T20:10:52.232621Z","title":"Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":17,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.232621Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:57e6b342fee3e90cf26b26d5c3074651eabd2935827deee8b7856df7d6b36cac","observation_id":"6a047561-fd14-403e-8c6a-1acc0d65edd3","resolution":{"observed_at":"2026-08-08T20:10:52.232621Z","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":"2026-08-08T20:10:53.064247Z","title":"Brax, brazilian labeled chest x-ray dataset,","venue":null,"work_id":"568ef453-00bd-412c-83f6-4c2c890e0ceb","year":2022},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":18,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.236703Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:b741df2e7e91671adf46febcf6a46cd7ab22f4aa3a303c0f181656c5562340cc","observation_id":"3fd68fab-2f50-4350-b7ef-7bc184a2df4d","resolution":{"observed_at":"2026-08-08T20:10:53.068945Z","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":"2026-08-08T20:10:53.049164Z","title":"Curation of the candid-ptx dataset with free-text reports,","venue":null,"work_id":"5f086271-7c4a-484b-97b1-08df1c11a730","year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":19,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.241405Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:b240149836df2ffe134e3cc6684ce11df3b11c34a8f997c7bc5cdd87655eb5ba","observation_id":"0ba8d44d-034a-4477-b2ac-8a261de8c090","resolution":{"observed_at":"2026-08-08T20:10:53.053969Z","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":{"arxiv_id":"2304.07193","last_updated":"2024-02-02T10:24:09Z","snapshot_observed_at":"2026-08-09T18:02:17.307812Z","submitted_at":"2023-04-14T15:12:19Z","title":"DINOv2: Learning Robust Visual Features without Supervision","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2304.07193","snapshot_observed_at":"2026-08-08T20:10:52.245637Z","title":"Dinov2: Learning robust visual features without supervision,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":20,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.245637Z"},"links":{"cited_paper":"/paper/2304.07193","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:b3fb725d1063c322162c1cbaadb4b86ab7f9696040113a47a079349af770eb3f","observation_id":"e02765db-46ec-4929-b35d-6775e6fdc4ff","resolution":{"observed_at":"2026-08-08T20:10:52.245637Z","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":"2026-08-08T20:10:52.250729Z","title":"CXR-LT 2024: Long-tailed, multi-label, and zero- shot classification on chest X-rays,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":21,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.250729Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:fb6dc799f19837ecae18606d02a7307d4e25436a82ad11bdf8ee647ede086690","observation_id":"bd7139de-7bf6-451c-9076-2b35f9185dd5","resolution":{"observed_at":"2026-08-08T20:10:52.250729Z","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":"2026-08-08T20:10:53.033273Z","title":"Lung cancer screening in the randomized prostate, lung, colorectal, and ovarian (plco) cancer screening trial,","venue":null,"work_id":"c04c4b0b-524a-472b-b9c3-5112e4dedb86","year":2010},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":22,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.255261Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:971fd8662cc6f9c69794151557d4bb042286d6c9f73e683a324f76f3930b8e94","observation_id":"fae78386-6e02-4b07-a702-3a6129533252","resolution":{"observed_at":"2026-08-08T20:10:53.038972Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:10:52.260015Z","title":"Vindr-cxr: An open dataset of chest x-rays with radiologist’s annotations,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":23,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.260015Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:bf0414ad541e8fe0b6f07f277a6301ade4979757e9a8daac1c7e81e8759ad1f1","observation_id":"b393fd49-379e-4b8b-bcdc-a7cc9df1bc79","resolution":{"observed_at":"2026-08-08T20:10:52.260015Z","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":"2026-08-08T20:10:53.009049Z","title":"Ranzcr clip - catheter and line position challenge,","venue":null,"work_id":"bcf91407-0993-4827-9cc0-9b4438ebdb2c","year":2020},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":24,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.264575Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:a7c5ff5e6c829f658ec671fc1ff01273ecfbc42b84c00f353e7001712ceb2c72","observation_id":"8eade645-93a5-405d-b9a4-85d8b1595937","resolution":{"observed_at":"2026-08-08T20:10:53.013808Z","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":{"arxiv_id":"2107.01327","last_updated":"2021-07-03T02:36:09Z","snapshot_observed_at":"2026-08-07T20:57:53.858786Z","submitted_at":"2021-07-03T02:36:09Z","title":"VinDr-RibCXR: A Benchmark Dataset for Automatic Segmentation and Labeling of Individual Ribs on Chest X-rays","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2107.01327","snapshot_observed_at":"2026-08-08T20:10:52.269046Z","title":"Vindr-ribcxr: A benchmark dataset for automatic segmentation and labeling of individual ribs on chest x-rays,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":25,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.269046Z"},"links":{"cited_paper":"/paper/2107.01327","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:2b73b916766c692e7a0d9aca703bd60497dfb373d9048e2512d4a9d8929c7ea8","observation_id":"fb034314-a460-4e38-b273-84d58fc1c344","resolution":{"observed_at":"2026-08-08T20:10:52.269046Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2106.08254","last_updated":"2022-09-03T14:11:33Z","snapshot_observed_at":"2026-07-06T11:19:34.705520Z","submitted_at":"2021-06-15T16:02:37Z","title":"BEiT: BERT Pre-Training of Image Transformers","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2106.08254","snapshot_observed_at":"2026-08-08T20:10:52.273884Z","title":"Beit: Bert pre-training of image transformers,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":26,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.273884Z"},"links":{"cited_paper":"/paper/2106.08254","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:c744f8d8c5a3253785b0a308da894dfa6f74aa31ee4bd35533f59e9520232d10","observation_id":"483deccc-9d7c-4c3d-837f-efa1b8ef4890","resolution":{"observed_at":"2026-08-08T20:10:52.273884Z","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":"2026-08-08T20:10:52.278724Z","title":"A simple framework for contrastive learning of visual representations,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":27,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.278724Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:7d07812f6fb52ea05f9c9129fae37152ad037b0b22b7d5d213edf720af0cac2a","observation_id":"96dbebbd-1bb7-4004-975e-3d7f2e196122","resolution":{"observed_at":"2026-08-08T20:10:52.278724Z","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":"2026-08-08T20:10:52.979629Z","title":"Towards a general-purpose foundation model for computational pathology,","venue":null,"work_id":"973bf35d-7393-47e3-bfd3-7d4e5d8129b9","year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":28,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.283321Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:553b8c3baa2835fdcbe80aae45a750d667e626b1da889cd7f8d9ee6bb6cc8749","observation_id":"aa53f888-0530-4660-801a-824611d36763","resolution":{"observed_at":"2026-08-08T20:10:52.988881Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:10:52.288374Z","title":"Bootstrap your own latent-a new approach to self-supervised learning,","venue":null,"work_id":null,"year":2020},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":29,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.288374Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:1b18a8e9849129a2b609ce2058fa7b0bd3ae6f9540c7794bca9b72b0b2cb946a","observation_id":"0e40c93c-131b-4a34-a8cb-7cdaa83a8838","resolution":{"observed_at":"2026-08-08T20:10:52.288374Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"1711.05225","last_updated":"2017-12-25T11:09:06Z","snapshot_observed_at":"2026-08-06T22:13:27.249372Z","submitted_at":"2017-11-14T17:58:50Z","title":"CheXNet: Radiologist-Level Pneumonia Detection on Chest X-Rays with Deep Learning","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"1711.05225","snapshot_observed_at":"2026-08-08T20:10:52.292723Z","title":"Chexnet: Radiologist- level pneumonia detection on chest x-rays with deep learning,","venue":null,"work_id":null,"year":2017},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":30,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.292723Z"},"links":{"cited_paper":"/paper/1711.05225","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:d70ac819ce7589a2f2f7af3bd9a35a0a904106328f373d82a6a17eaae09343d8","observation_id":"b50c159b-46e0-437f-955a-936734693fd0","resolution":{"observed_at":"2026-08-08T20:10:52.292723Z","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":"2026-08-08T20:10:52.953800Z","title":"Chex- transfer: performance and parameter efficiency of imagenet models for chest x-ray interpretation,","venue":null,"work_id":"a46b734e-13a4-4333-b44e-a3d238f4712b","year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":31,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.297409Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:d06cb442905c720ff9f0a31c75574336563d5c8068193c379acc8ad19dfd9697","observation_id":"529adf85-313b-4e7f-b905-77b292f362ed","resolution":{"observed_at":"2026-08-08T20:10:52.959478Z","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":"2026-08-08T20:10:52.937685Z","title":"Medaug: Contrastive learning leveraging patient metadata improves representations for chest x-ray interpretation,","venue":null,"work_id":"4ea70a5e-1bde-479c-bd11-76372edb499c","year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":32,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.301516Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:be873cc78eefcc8a72776c419ea7726d2156b65deaf55fe596b29dbb8ced6af7","observation_id":"aac089a5-b75a-44cc-bf6f-5a759a01a542","resolution":{"observed_at":"2026-08-08T20:10:52.942777Z","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":"2026-08-08T20:10:52.922326Z","title":"Moco pretraining improves representation and transferability of chest x-ray models,","venue":null,"work_id":"3c2cfa06-d9f2-424d-9860-73cadb0d6aa0","year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":33,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.306116Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:d499333ccb2eb9b181a4a6815b5a1dd33666d363631b9d938ae78a08067d2c47","observation_id":"a177631d-446b-4d7c-82df-6ab45369e393","resolution":{"observed_at":"2026-08-08T20:10:52.927642Z","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":{"arxiv_id":"2405.01469","last_updated":"2024-05-02T16:59:10Z","snapshot_observed_at":"2026-07-06T18:08:56.480769Z","submitted_at":"2024-05-02T16:59:10Z","title":"Advancing human-centric AI for robust X-ray analysis through holistic self-supervised learning","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.01469","snapshot_observed_at":"2026-08-08T20:10:52.310208Z","title":"Advancing human-centric ai for robust x-ray analysis through holistic self-supervised learning,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":34,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.310208Z"},"links":{"cited_paper":"/paper/2405.01469","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:933ba72fd9eccb7a6276b395f07f597f87c3637b28ff29782e26c567cbdd0e6c","observation_id":"c96e94d6-e035-4f25-b17d-a5460c6bc044","resolution":{"observed_at":"2026-08-08T20:10:52.310208Z","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":"2026-08-08T20:10:52.907120Z","title":"Delving into masked autoen- coders for multi-label thorax disease classification,","venue":null,"work_id":"f9e552cf-82e5-4a3b-82b1-4ec1972f3ef6","year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":35,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.314054Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:ddea1ee090c16b183914d5db796b75d10b26dfbb6422e6cb32bb5ea6411d5269","observation_id":"16506303-6950-426f-8f66-2c93a237704f","resolution":{"observed_at":"2026-08-08T20:10:52.911404Z","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":"2026-08-08T20:10:52.892473Z","title":"Foundation ark: Accruing and reusing knowledge for superior and robust performance,","venue":null,"work_id":"9fbec353-fae2-4836-b2c6-692d9914a7ff","year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":36,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.318989Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:4aad36391651fb69df4e84fde0dabbf217bb810045f5e2085944dd46477c8feb","observation_id":"06bc1bb0-20eb-4d3d-9dc0-6bf959ed92ee","resolution":{"observed_at":"2026-08-08T20:10:52.897412Z","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":"2026-08-08T20:10:52.876994Z","title":"Towards foundation models learned from anatomy in medical imaging via self- supervision,","venue":null,"work_id":"748cef9b-cd1c-476d-886b-b069c3adb171","year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":37,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.324424Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:22e38b98a3c0b6292dd34855e747753aa7984278ca8b06760dcd63c65272834e","observation_id":"666d501e-18ac-4497-8ea6-a9983e34587f","resolution":{"observed_at":"2026-08-08T20:10:52.882086Z","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":{"arxiv_id":"2303.00915","last_updated":"2025-01-08T22:58:51Z","snapshot_observed_at":"2026-07-06T14:57:39.647497Z","submitted_at":"2023-03-02T02:20:04Z","title":"BiomedCLIP: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2303.00915","snapshot_observed_at":"2026-08-08T20:10:52.328935Z","title":"Biomedclip: a multimodal biomedical foundation model pretrained from fifteen million scientific image-text pairs,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":38,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.328935Z"},"links":{"cited_paper":"/paper/2303.00915","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:043d81e7ed9bd44fce80ed102d03fa05d3b52126c37c7c92785e403429517826","observation_id":"85bb61ef-97d0-4cdf-a63a-ab7c49c0f8c5","resolution":{"observed_at":"2026-08-08T20:10:52.328935Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2112.13906","last_updated":"2021-12-27T21:19:23Z","snapshot_observed_at":"2026-08-05T23:11:14.362732Z","submitted_at":"2021-12-27T21:19:23Z","title":"Does CLIP Benefit Visual Question Answering in the Medical Domain as Much as it Does in the General Domain?","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2112.13906","snapshot_observed_at":"2026-08-08T20:10:52.333997Z","title":"Does clip benefit visual question answering in the medical domain as much as it does in the general domain?","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":39,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.333997Z"},"links":{"cited_paper":"/paper/2112.13906","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:f019e27c4baa7a67752508c43e008d7c31091adff9b936ccb5d1db2218c925a4","observation_id":"969c01af-5edb-4cb7-b6e5-d359eb2c90ad","resolution":{"observed_at":"2026-08-08T20:10:52.333997Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2401.12208","last_updated":"2024-12-18T20:56:18Z","snapshot_observed_at":"2026-08-07T08:31:58.372526Z","submitted_at":"2024-01-22T18:51:07Z","title":"A Vision-Language Foundation Model to Enhance Efficiency of Chest X-ray Interpretation","version":2},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2401.12208","snapshot_observed_at":"2026-08-08T20:10:52.338921Z","title":"Chexagent: Towards a foundation model for chest x-ray interpretation,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":40,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.338921Z"},"links":{"cited_paper":"/paper/2401.12208","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:875fda036c9ced8cd7be3ef8499ec7aaf01332e132e32092858f6c01acdd51da","observation_id":"03e91c85-a745-4890-8d8f-e5c2a9ebe89d","resolution":{"observed_at":"2026-08-08T20:10:52.338921Z","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":"2026-08-08T20:10:52.344073Z","title":"Llava-med: Training a large language-and-vision assistant for biomedicine in one day,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":41,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.344073Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:6a09467dcab8ed5854c936ebe91d82d331b102102a1a7b2fc007912779e66530","observation_id":"7a836fc9-edab-49cc-8be8-cd9ed1e141f0","resolution":{"observed_at":"2026-08-08T20:10:52.344073Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}},{"citation":{"cited_paper":{"arxiv_id":"2405.03162","last_updated":"2024-05-06T04:44:22Z","snapshot_observed_at":"2026-07-06T18:10:12.621492Z","submitted_at":"2024-05-06T04:44:22Z","title":"Advancing Multimodal Medical Capabilities of Gemini","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2405.03162","snapshot_observed_at":"2026-08-08T20:10:52.348577Z","title":"Advancing multi- modal medical capabilities of gemini,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":42,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.348577Z"},"links":{"cited_paper":"/paper/2405.03162","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:25cef7c0bac12cd892a55a9c2f146e7cdcbf1ce9b04cfce1b0b6024bb6d2ac57","observation_id":"b92499e4-13cb-4878-a1a7-b791ab7bd523","resolution":{"observed_at":"2026-08-08T20:10:52.348577Z","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":"2026-08-08T20:10:52.850762Z","title":"Siim-acr pneumothorax seg- mentation,","venue":null,"work_id":"ebfd5ab0-05a5-4fab-a29d-6e5ad03ec55f","year":2019},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":43,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.353253Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:a1f8b5582dc5c7e237d21a157297e6ae4be68e7dfb3c2fce5833ac478a321831","observation_id":"0aabc2b6-d16f-4687-b7e3-eb019df4ddfb","resolution":{"observed_at":"2026-08-08T20:10:52.855909Z","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":"2026-08-08T20:10:52.835904Z","title":"Object-cxr - automatic detection of foreign objects on chest x-rays,","venue":null,"work_id":"e3d26993-a470-4be9-84e7-56e8e41749b4","year":2020},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":44,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.358207Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:6af2b291ac0db6fe04be0d5ef169176cf74383995f652403a5d20074e6460f5c","observation_id":"5f2435b0-ec52-476c-8687-3f9c8acd42d5","resolution":{"observed_at":"2026-08-08T20:10:52.840581Z","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":"2026-08-08T20:10:52.820119Z","title":"The 2021 siim-fisabio-rsna machine learning covid-19 challenge: An- notation and standard exam classification of covid-19 chest radiographs,","venue":null,"work_id":"f21699e5-60e4-4802-9961-2337df970fc9","year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":45,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.362976Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:83eb93783e9a8ecf715926587e3bb95bf6f2e6f4b3bd0e8d4e986c9ce61f2384","observation_id":"0d554694-afd4-4730-8200-46dbe9159f2c","resolution":{"observed_at":"2026-08-08T20:10:52.825898Z","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":{"arxiv_id":"2311.17677","last_updated":"2023-11-29T14:40:31Z","snapshot_observed_at":"2026-07-06T16:54:26.205948Z","submitted_at":"2023-11-29T14:40:31Z","title":"COVIDx CXR-4: An Expanded Multi-Institutional Open-Source Benchmark Dataset for Chest X-ray Image-Based Computer-Aided COVID-19 Diagnostics","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2311.17677","snapshot_observed_at":"2026-08-08T20:10:52.367564Z","title":"Covidx cxr-4: An expanded multi-institutional open-source benchmark dataset for chest x- ray image-based computer-aided covid-19 diagnostics,","venue":null,"work_id":null,"year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":46,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.367564Z"},"links":{"cited_paper":"/paper/2311.17677","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:6680eb6a36b35586002769bb1bd31d0949c0a0c8a46b2c5eae77b7d03984a0e3","observation_id":"42b9f43b-1012-4f73-be6e-137e74346e04","resolution":{"observed_at":"2026-08-08T20:10:52.367564Z","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":"2026-08-08T20:10:52.804966Z","title":"Midrc covidx challenge,","venue":null,"work_id":"f7cfdb6d-1102-44b4-894e-ac3007166095","year":2022},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":47,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.372335Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:2b0bfc52c58e985a976be7335cdba0d897183e41fe79731a62a00ebefbd176d6","observation_id":"1f526013-937e-405f-84dc-0d9f49b8664b","resolution":{"observed_at":"2026-08-08T20:10:52.810007Z","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":{"arxiv_id":"2006.01174","last_updated":"2020-06-05T12:53:43Z","snapshot_observed_at":"2026-08-09T22:31:19.143387Z","submitted_at":"2020-06-01T18:06:21Z","title":"BIMCV COVID-19+: a large annotated dataset of RX and CT images from COVID-19 patients","version":3},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2006.01174","snapshot_observed_at":"2026-08-08T20:10:52.377085Z","title":"Bimcv covid-19+: a large annotated dataset of rx and ct images from covid-19 patients,","venue":null,"work_id":null,"year":2006},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":48,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.377085Z"},"links":{"cited_paper":"/paper/2006.01174","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:2a8f1749744f9c6e1fe0565f3e8800a6dc2f68c9121e8555ca7e0137cda9b76d","observation_id":"5f09bb6e-7781-4657-928f-6532982933aa","resolution":{"observed_at":"2026-08-08T20:10:52.377085Z","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":"2026-08-08T20:10:52.790195Z","title":"Learning transferable visual models from natural language supervision,","venue":null,"work_id":"dc8b9cdc-7ff0-470d-882a-c2b0aef02bd3","year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":49,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.381684Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:c0b2f48b63cb1f437df43fa192a971bdc40272661033a822673756c1d0c68b21","observation_id":"9f541619-bd21-4169-acd5-eacffe262418","resolution":{"observed_at":"2026-08-08T20:10:52.795108Z","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":{"arxiv_id":"2404.08471","last_updated":"2024-02-15T18:59:11Z","snapshot_observed_at":"2026-08-07T02:30:11.447693Z","submitted_at":"2024-02-15T18:59:11Z","title":"Revisiting Feature Prediction for Learning Visual Representations from Video","version":1},"cited_work":{"arxiv_id":null,"doi":null,"metadata_source":null,"pith_arxiv_id":"2404.08471","snapshot_observed_at":"2026-08-08T20:10:52.385989Z","title":"Revisiting feature prediction for learning visual representations from video,","venue":null,"work_id":null,"year":2024},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":50,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.385989Z"},"links":{"cited_paper":"/paper/2404.08471","citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:bdfc5f840abc2d4ddc7bd1ee82291ce450dfacebc304eaf2eabf394347a563d6","observation_id":"6a1de091-0a5d-4434-af74-d68a622d1079","resolution":{"observed_at":"2026-08-08T20:10:52.385989Z","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":"2026-08-08T20:10:52.391106Z","title":"Gloria: A multimodal global-local representation learning framework for label- efficient medical image recognition,","venue":null,"work_id":null,"year":2021},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":51,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.391106Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:571f84a07abdd51d5e9339feaa2941db8b8b6dcc21627d1b27f85346b72748af","observation_id":"45ae2cbe-180c-47b1-8bf8-6f0ead2edbf0","resolution":{"observed_at":"2026-08-08T20:10:52.391106Z","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":"2026-08-08T20:10:52.764156Z","title":"Identifying medical diagnoses and treatable diseases by image-based deep learning,","venue":null,"work_id":"bb379b23-40fd-4022-98b9-40a3287b497b","year":2018},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":52,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.396012Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:64a13158b94a0e686a903985e7e062da80f2ce4a17b056d2b3c4f2f9a13c790f","observation_id":"245335f2-c114-47d0-be1d-dfc8ad00aeb7","resolution":{"observed_at":"2026-08-08T20:10:52.769413Z","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":"2026-08-08T20:10:52.747103Z","title":"Two public chest x-ray datasets for computer-aided screen- ing of pulmonary diseases,","venue":null,"work_id":"a5a8cb62-7989-4e11-94e5-90618e2cf683","year":2014},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":53,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.401487Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:fcdca9cf064156a2effe51b16bc14e02055a9b2a108ce6422f6ab2350dae4a8f","observation_id":"824b8c09-8213-47f3-9752-2998d2ceb786","resolution":{"observed_at":"2026-08-08T20:10:52.753211Z","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":"2026-08-08T20:10:52.730757Z","title":"Shiraishi, S","venue":null,"work_id":"e378a116-142e-49cf-9007-c1b090db88f7","year":2000},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":54,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.406228Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:1e2e15d0b66d3abec9278d249177066e0ee56e49bcadf49e7a3a3d676e900e69","observation_id":"4a0110f6-f84c-497b-ad6a-2e40346f36aa","resolution":{"observed_at":"2026-08-08T20:10:52.736438Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:10:52.410939Z","title":"A convnet for the 2020s,","venue":null,"work_id":null,"year":2022},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":55,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.410939Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:16080618561c044f59901bf291f46fdb6675a2359da30e6854945450136ab554","observation_id":"b722595b-0a1a-45f5-88a5-9beaba1b1e77","resolution":{"observed_at":"2026-08-08T20:10:52.410939Z","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":"2026-08-08T20:10:52.705941Z","title":"Eva: Exploring the limits of masked visual representation learning at scale,","venue":null,"work_id":"0c8bf93e-4d67-4809-9e81-926b3b18366d","year":2023},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":56,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.416466Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:a16a4b53286026b13a777394baefe6c96a53d643253f4417c238e631ab50ba6c","observation_id":"edac445c-2cbd-4855-9142-78b453d1197a","resolution":{"observed_at":"2026-08-08T20:10:52.710874Z","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":"2026-08-08T20:10:52.689272Z","title":"Screening by chest radiograph and lung cancer mortality: the prostate, lung, colorectal, and ovarian (plco) randomized trial,","venue":null,"work_id":"50f007b6-7be4-4a02-b7bd-3489106f5437","year":2011},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":57,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.421484Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:e269dfa846852d4b2fffcc814bf313539920a183efbcf68d9a83c479716c6cfb","observation_id":"172672fc-d10a-4c1d-807d-68cb5f48db58","resolution":{"observed_at":"2026-08-08T20:10:52.695573Z","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":null,"pith_arxiv_id":null,"snapshot_observed_at":"2026-08-08T20:10:52.426326Z","title":"Unified perceptual parsing for scene understanding,","venue":null,"work_id":null,"year":2018},"citing_paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration","version":2},"reference_index":58,"source":"pdf_text","source_observed_at":"2026-08-08T20:10:52.426326Z"},"links":{"citing_paper":"/paper/2502.05142"},"observation_digest":"sha256:ce45f57f952e607e16726aaeb22e78a5fa14a4a1496aec9134f5cf9a2c250547","observation_id":"5c9fb8ee-6f4a-4d08-b560-da07b3d05b0b","resolution":{"observed_at":"2026-08-08T20:10:52.426326Z","resolver_source":null,"status":"unresolved"},"standing_notice":{"events":[],"reason":"canonical_work_link_unavailable","source_receipts":[],"state":"unavailable"}}],"paper":{"arxiv_id":"2502.05142","last_updated":"2025-06-19T14:25:55Z","latest_version":2,"primary_category":"eess.IV","snapshot_observed_at":"2026-08-09T13:19:18.259577Z","submitted_at":"2025-02-07T18:16:15Z","title":"Chest X-ray Foundation Model with Global and Local Representations Integration"},"reference_resolution":{"displayed":58,"state_counts":{"malformed_identifier":0,"metadata_mismatch":0,"parse_uncertain":0,"unresolved":31,"verified_exact":0,"verified_fuzzy":27},"total_outbound_references":58},"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 58 of 58 outbound references and 1 inbound Pith citation observation for arXiv:2502.05142."}