{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:UACOYOS66RV3NDI5GRDN5M7L2E","short_pith_number":"pith:UACOYOS6","schema_version":"1.0","canonical_sha256":"a004ec3a5ef46bb68d1d3446deb3ebd11fa3ddf6309eee57a12ce9d96db4e93e","source":{"kind":"arxiv","id":"2607.25503","version":1},"attestation_state":"computed","paper":{"title":"Group Equivariant Diffusion for Anomaly Detection in Computational Cytology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anirban Mukhopadhyay, Ssharvien Kumar Sivakumar, Swarnadip Chatterjee","submitted_at":"2026-07-28T09:38:05Z","abstract_excerpt":"Computational cytology on whole-slide images is challenging because malignant cells are rare, heterogeneous, and annotated slides are scarce. Anomaly detection frameworks can be trained on normal slide-negative patches and then applied at test time to flag abnormal patches in held-out slides. Most unsupervised anomaly detection approaches including generative ones (GAN-based and diffusion-based), are tuned to organ-level imaging and require large curated datasets. In cytology the signal is cell-centric: rotating or flipping a single-cell patch does not change its diagnostic class, yet standard"},"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":"2607.25503","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2026-07-28T09:38:05Z","cross_cats_sorted":[],"title_canon_sha256":"80d6b9fdee00f23b39925c97df7238346e510b8521e85483ef318c1fe0b9747e","abstract_canon_sha256":"81b66174efea9bb226ffda261968f3abcb71dfd2f598a92971a6796fc956551b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-29T01:25:18.613227Z","signature_b64":"tmdxDEIT+q+6g/zI5iuv4EUxQYYGIwwbR2oTn1NuITet352GmjFWByEFrsvV3EUESpE5DK6x+KERKl+2UMJcBA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"a004ec3a5ef46bb68d1d3446deb3ebd11fa3ddf6309eee57a12ce9d96db4e93e","last_reissued_at":"2026-07-29T01:25:18.612366Z","signature_status":"signed_v1","first_computed_at":"2026-07-29T01:25:18.612366Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Group Equivariant Diffusion for Anomaly Detection in Computational Cytology","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Anirban Mukhopadhyay, Ssharvien Kumar Sivakumar, Swarnadip Chatterjee","submitted_at":"2026-07-28T09:38:05Z","abstract_excerpt":"Computational cytology on whole-slide images is challenging because malignant cells are rare, heterogeneous, and annotated slides are scarce. Anomaly detection frameworks can be trained on normal slide-negative patches and then applied at test time to flag abnormal patches in held-out slides. Most unsupervised anomaly detection approaches including generative ones (GAN-based and diffusion-based), are tuned to organ-level imaging and require large curated datasets. In cytology the signal is cell-centric: rotating or flipping a single-cell patch does not change its diagnostic class, yet standard"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.25503","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/2607.25503/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":"2607.25503","created_at":"2026-07-29T01:25:18.612822+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.25503v1","created_at":"2026-07-29T01:25:18.612822+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.25503","created_at":"2026-07-29T01:25:18.612822+00:00"},{"alias_kind":"pith_short_12","alias_value":"UACOYOS66RV3","created_at":"2026-07-29T01:25:18.612822+00:00"},{"alias_kind":"pith_short_16","alias_value":"UACOYOS66RV3NDI5","created_at":"2026-07-29T01:25:18.612822+00:00"},{"alias_kind":"pith_short_8","alias_value":"UACOYOS6","created_at":"2026-07-29T01:25:18.612822+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/UACOYOS66RV3NDI5GRDN5M7L2E","json":"https://pith.science/pith/UACOYOS66RV3NDI5GRDN5M7L2E.json","graph_json":"https://pith.science/api/pith-number/UACOYOS66RV3NDI5GRDN5M7L2E/graph.json","events_json":"https://pith.science/api/pith-number/UACOYOS66RV3NDI5GRDN5M7L2E/events.json","paper":"https://pith.science/paper/UACOYOS6"},"agent_actions":{"view_html":"https://pith.science/pith/UACOYOS66RV3NDI5GRDN5M7L2E","download_json":"https://pith.science/pith/UACOYOS66RV3NDI5GRDN5M7L2E.json","view_paper":"https://pith.science/paper/UACOYOS6","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.25503&json=true","fetch_graph":"https://pith.science/api/pith-number/UACOYOS66RV3NDI5GRDN5M7L2E/graph.json","fetch_events":"https://pith.science/api/pith-number/UACOYOS66RV3NDI5GRDN5M7L2E/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/UACOYOS66RV3NDI5GRDN5M7L2E/action/timestamp_anchor","attest_storage":"https://pith.science/pith/UACOYOS66RV3NDI5GRDN5M7L2E/action/storage_attestation","attest_author":"https://pith.science/pith/UACOYOS66RV3NDI5GRDN5M7L2E/action/author_attestation","sign_citation":"https://pith.science/pith/UACOYOS66RV3NDI5GRDN5M7L2E/action/citation_signature","submit_replication":"https://pith.science/pith/UACOYOS66RV3NDI5GRDN5M7L2E/action/replication_record"}},"created_at":"2026-07-29T01:25:18.612822+00:00","updated_at":"2026-07-29T01:25:18.612822+00:00"}