{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:N35EYAVU7U5T7S3AORXOHOLJPO","short_pith_number":"pith:N35EYAVU","schema_version":"1.0","canonical_sha256":"6efa4c02b4fd3b3fcb60746ee3b9697bae832d12d0dd0c3856c8af41b74d67be","source":{"kind":"arxiv","id":"2504.06205","version":2},"attestation_state":"computed","paper":{"title":"HER-Seg: Holistically Efficient Segmentation for High-Resolution Medical Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chenxin Li, Qing Xu, Rong Qu, Tesema Fiseha Berhanu, Wenting Duan, Xiangjian He, Yue Li, Zhen Chen, Zhenye Lou","submitted_at":"2025-04-08T16:48:57Z","abstract_excerpt":"High-resolution segmentation is critical for precise disease diagnosis by extracting fine-grained morphological details. Existing hierarchical encoder-decoder frameworks have demonstrated remarkable adaptability across diverse medical segmentation tasks. While beneficial, they usually require the huge computation and memory cost when handling large-size segmentation, which limits their applications in foundation model building and real-world clinical scenarios. To address this limitation, we propose a holistically efficient framework for high-resolution medical image segmentation, called HER-S"},"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":"2504.06205","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2025-04-08T16:48:57Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"134df1430ecdcf33351670b01d6f361bb8547883f15bbcc18f63b58ebb9b86be","abstract_canon_sha256":"4bfe94975697bdb14a68721688627a2cdd9f49d04af76cfea23895cbd1ba2c74"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:40:33.834770Z","signature_b64":"3h//6gcSCU/4OuAuaoOyXtrHv/LTjRAZNDEwvB5brWBAJ/9XTy2p1/tO05I7X83y+fMnRO3ZQlWs4tH/+th1CQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6efa4c02b4fd3b3fcb60746ee3b9697bae832d12d0dd0c3856c8af41b74d67be","last_reissued_at":"2026-07-05T11:40:33.834115Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:40:33.834115Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"HER-Seg: Holistically Efficient Segmentation for High-Resolution Medical Images","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Chenxin Li, Qing Xu, Rong Qu, Tesema Fiseha Berhanu, Wenting Duan, Xiangjian He, Yue Li, Zhen Chen, Zhenye Lou","submitted_at":"2025-04-08T16:48:57Z","abstract_excerpt":"High-resolution segmentation is critical for precise disease diagnosis by extracting fine-grained morphological details. Existing hierarchical encoder-decoder frameworks have demonstrated remarkable adaptability across diverse medical segmentation tasks. While beneficial, they usually require the huge computation and memory cost when handling large-size segmentation, which limits their applications in foundation model building and real-world clinical scenarios. To address this limitation, we propose a holistically efficient framework for high-resolution medical image segmentation, called HER-S"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.06205","kind":"arxiv","version":2},"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/2504.06205/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":"2504.06205","created_at":"2026-07-05T11:40:33.834194+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.06205v2","created_at":"2026-07-05T11:40:33.834194+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.06205","created_at":"2026-07-05T11:40:33.834194+00:00"},{"alias_kind":"pith_short_12","alias_value":"N35EYAVU7U5T","created_at":"2026-07-05T11:40:33.834194+00:00"},{"alias_kind":"pith_short_16","alias_value":"N35EYAVU7U5T7S3A","created_at":"2026-07-05T11:40:33.834194+00:00"},{"alias_kind":"pith_short_8","alias_value":"N35EYAVU","created_at":"2026-07-05T11:40:33.834194+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2509.06740","citing_title":"Co-Seg: Mutual Prompt-Guided Collaborative Learning for Tissue and Nuclei Segmentation","ref_index":34,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO","json":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO.json","graph_json":"https://pith.science/api/pith-number/N35EYAVU7U5T7S3AORXOHOLJPO/graph.json","events_json":"https://pith.science/api/pith-number/N35EYAVU7U5T7S3AORXOHOLJPO/events.json","paper":"https://pith.science/paper/N35EYAVU"},"agent_actions":{"view_html":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO","download_json":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO.json","view_paper":"https://pith.science/paper/N35EYAVU","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.06205&json=true","fetch_graph":"https://pith.science/api/pith-number/N35EYAVU7U5T7S3AORXOHOLJPO/graph.json","fetch_events":"https://pith.science/api/pith-number/N35EYAVU7U5T7S3AORXOHOLJPO/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO/action/timestamp_anchor","attest_storage":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO/action/storage_attestation","attest_author":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO/action/author_attestation","sign_citation":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO/action/citation_signature","submit_replication":"https://pith.science/pith/N35EYAVU7U5T7S3AORXOHOLJPO/action/replication_record"}},"created_at":"2026-07-05T11:40:33.834194+00:00","updated_at":"2026-07-05T11:40:33.834194+00:00"}