{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:RPHDDRWEB6VXMVPDUURHAINLXD","short_pith_number":"pith:RPHDDRWE","schema_version":"1.0","canonical_sha256":"8bce31c6c40fab7655e3a5227021abb8ed4ca955531b529832afd36acad18883","source":{"kind":"arxiv","id":"2504.12718","version":1},"attestation_state":"computed","paper":{"title":"TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexandra Getmanskaya, Evgeniy Vasilyev, Vadim Turlapov, Walid Rehamnia","submitted_at":"2025-04-17T07:48:05Z","abstract_excerpt":"Digital pathology, augmented by artificial intelligence (AI), holds significant promise for improving the workflow of pathologists. However, challenges such as the labor-intensive annotation of whole slide images (WSIs), high computational demands, and trust concerns arising from the absence of uncertainty estimation in predictions hinder the practical application of current AI methodologies in histopathology. To address these issues, we present a novel trustful fully unsupervised multi-level segmentation methodology (TUMLS) for WSIs. TUMLS adopts an autoencoder (AE) as a feature extractor to "},"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":"2504.12718","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-04-17T07:48:05Z","cross_cats_sorted":["cs.AI","cs.CV"],"title_canon_sha256":"d1594eccd803a837bf0e59ee463a7377405a82af2603a1eddccb4708c294b505","abstract_canon_sha256":"1ced5bd817ebdd04c6578d94f5b8cffcdc1b0fc30cd37a6974744391157cfde7"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:50:21.224669Z","signature_b64":"7x8KlQIZhmzgXC/tBosUjTmnVaorhi5D8am3wA8jbrklK8YkYfuHMmMN8uI3zklibk39bOg3nltRfhUS0rKhCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8bce31c6c40fab7655e3a5227021abb8ed4ca955531b529832afd36acad18883","last_reissued_at":"2026-07-05T10:50:21.224248Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:50:21.224248Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"TUMLS: Trustful Fully Unsupervised Multi-Level Segmentation for Whole Slide Images of Histology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI","cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexandra Getmanskaya, Evgeniy Vasilyev, Vadim Turlapov, Walid Rehamnia","submitted_at":"2025-04-17T07:48:05Z","abstract_excerpt":"Digital pathology, augmented by artificial intelligence (AI), holds significant promise for improving the workflow of pathologists. However, challenges such as the labor-intensive annotation of whole slide images (WSIs), high computational demands, and trust concerns arising from the absence of uncertainty estimation in predictions hinder the practical application of current AI methodologies in histopathology. To address these issues, we present a novel trustful fully unsupervised multi-level segmentation methodology (TUMLS) for WSIs. TUMLS adopts an autoencoder (AE) as a feature extractor to "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.12718","kind":"arxiv","version":1},"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/2504.12718/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":"2504.12718","created_at":"2026-07-05T10:50:21.224304+00:00"},{"alias_kind":"arxiv_version","alias_value":"2504.12718v1","created_at":"2026-07-05T10:50:21.224304+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.12718","created_at":"2026-07-05T10:50:21.224304+00:00"},{"alias_kind":"pith_short_12","alias_value":"RPHDDRWEB6VX","created_at":"2026-07-05T10:50:21.224304+00:00"},{"alias_kind":"pith_short_16","alias_value":"RPHDDRWEB6VXMVPD","created_at":"2026-07-05T10:50:21.224304+00:00"},{"alias_kind":"pith_short_8","alias_value":"RPHDDRWE","created_at":"2026-07-05T10:50:21.224304+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/RPHDDRWEB6VXMVPDUURHAINLXD","json":"https://pith.science/pith/RPHDDRWEB6VXMVPDUURHAINLXD.json","graph_json":"https://pith.science/api/pith-number/RPHDDRWEB6VXMVPDUURHAINLXD/graph.json","events_json":"https://pith.science/api/pith-number/RPHDDRWEB6VXMVPDUURHAINLXD/events.json","paper":"https://pith.science/paper/RPHDDRWE"},"agent_actions":{"view_html":"https://pith.science/pith/RPHDDRWEB6VXMVPDUURHAINLXD","download_json":"https://pith.science/pith/RPHDDRWEB6VXMVPDUURHAINLXD.json","view_paper":"https://pith.science/paper/RPHDDRWE","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2504.12718&json=true","fetch_graph":"https://pith.science/api/pith-number/RPHDDRWEB6VXMVPDUURHAINLXD/graph.json","fetch_events":"https://pith.science/api/pith-number/RPHDDRWEB6VXMVPDUURHAINLXD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/RPHDDRWEB6VXMVPDUURHAINLXD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/RPHDDRWEB6VXMVPDUURHAINLXD/action/storage_attestation","attest_author":"https://pith.science/pith/RPHDDRWEB6VXMVPDUURHAINLXD/action/author_attestation","sign_citation":"https://pith.science/pith/RPHDDRWEB6VXMVPDUURHAINLXD/action/citation_signature","submit_replication":"https://pith.science/pith/RPHDDRWEB6VXMVPDUURHAINLXD/action/replication_record"}},"created_at":"2026-07-05T10:50:21.224304+00:00","updated_at":"2026-07-05T10:50:21.224304+00:00"}