{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:FXQJFNKFGUAH7CDQQHKNMGJSRS","short_pith_number":"pith:FXQJFNKF","schema_version":"1.0","canonical_sha256":"2de092b54535007f887081d4d619328cb6bc4e0221cfeb1a0e0e64d86699ffe9","source":{"kind":"arxiv","id":"2005.13201","version":4},"attestation_state":"computed","paper":{"title":"Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Adam P Harrison, Ashwin Raju, ChienHuang Liao, Chi-Tung Cheng, Jing Xiao, Jinzheng Cai, Junzhou Huang, Le Lu, Yunakai Huo","submitted_at":"2020-05-27T06:58:39Z","abstract_excerpt":"In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of "},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2005.13201","kind":"arxiv","version":4},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-05-27T06:58:39Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"57388d324d2062ec47b7937700464d4dc6f7f2bd42db227166ebb1e5cb000dab","abstract_canon_sha256":"910ea5d1274a6c2077ef62aa7dd2ea800cb2bcb859f0a0b01c12cce46b253b56"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:59:02.490333Z","signature_b64":"F4bF9sQu+pk4RqEANRLKMZM7r56jMs+FAIJyfPnWnjZGV62TIWxCvXbWsdOQ57HuGLxBBzXZPi0mN9gdBR7SCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"2de092b54535007f887081d4d619328cb6bc4e0221cfeb1a0e0e64d86699ffe9","last_reissued_at":"2026-07-05T02:59:02.489854Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:59:02.489854Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Co-Heterogeneous and Adaptive Segmentation from Multi-Source and Multi-Phase CT Imaging Data: A Study on Pathological Liver and Lesion Segmentation","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Adam P Harrison, Ashwin Raju, ChienHuang Liao, Chi-Tung Cheng, Jing Xiao, Jinzheng Cai, Junzhou Huang, Le Lu, Yunakai Huo","submitted_at":"2020-05-27T06:58:39Z","abstract_excerpt":"In medical imaging, organ/pathology segmentation models trained on current publicly available and fully-annotated datasets usually do not well-represent the heterogeneous modalities, phases, pathologies, and clinical scenarios encountered in real environments. On the other hand, there are tremendous amounts of unlabelled patient imaging scans stored by many modern clinical centers. In this work, we present a novel segmentation strategy, co-heterogenous and adaptive segmentation (CHASe), which only requires a small labeled cohort of single phase imaging data to adapt to any unlabeled cohort of "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2005.13201","kind":"arxiv","version":4},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2005.13201/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2005.13201","created_at":"2026-07-05T02:59:02.489915+00:00"},{"alias_kind":"arxiv_version","alias_value":"2005.13201v4","created_at":"2026-07-05T02:59:02.489915+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2005.13201","created_at":"2026-07-05T02:59:02.489915+00:00"},{"alias_kind":"pith_short_12","alias_value":"FXQJFNKFGUAH","created_at":"2026-07-05T02:59:02.489915+00:00"},{"alias_kind":"pith_short_16","alias_value":"FXQJFNKFGUAH7CDQ","created_at":"2026-07-05T02:59:02.489915+00:00"},{"alias_kind":"pith_short_8","alias_value":"FXQJFNKF","created_at":"2026-07-05T02:59:02.489915+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS","json":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS.json","graph_json":"https://pith.science/api/pith-number/FXQJFNKFGUAH7CDQQHKNMGJSRS/graph.json","events_json":"https://pith.science/api/pith-number/FXQJFNKFGUAH7CDQQHKNMGJSRS/events.json","paper":"https://pith.science/paper/FXQJFNKF"},"agent_actions":{"view_html":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS","download_json":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS.json","view_paper":"https://pith.science/paper/FXQJFNKF","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2005.13201&json=true","fetch_graph":"https://pith.science/api/pith-number/FXQJFNKFGUAH7CDQQHKNMGJSRS/graph.json","fetch_events":"https://pith.science/api/pith-number/FXQJFNKFGUAH7CDQQHKNMGJSRS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS/action/storage_attestation","attest_author":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS/action/author_attestation","sign_citation":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS/action/citation_signature","submit_replication":"https://pith.science/pith/FXQJFNKFGUAH7CDQQHKNMGJSRS/action/replication_record"}},"created_at":"2026-07-05T02:59:02.489915+00:00","updated_at":"2026-07-05T02:59:02.489915+00:00"}