{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:5CHE7BW2BFH6FXLPV75XJGVGCE","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"2019579d8f54ba6a2e032fa3bcfe6aac74bf2f2b93ede5a4c89757dd39711ef8","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2023-06-27T10:03:15Z","title_canon_sha256":"2885bfa7f81fca25d4a627a098a4b9e15bc272bbf6224767e80e38ebffbc898b"},"schema_version":"1.0","source":{"id":"2306.15350","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2306.15350","created_at":"2026-07-05T06:57:54Z"},{"alias_kind":"arxiv_version","alias_value":"2306.15350v2","created_at":"2026-07-05T06:57:54Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.15350","created_at":"2026-07-05T06:57:54Z"},{"alias_kind":"pith_short_12","alias_value":"5CHE7BW2BFH6","created_at":"2026-07-05T06:57:54Z"},{"alias_kind":"pith_short_16","alias_value":"5CHE7BW2BFH6FXLP","created_at":"2026-07-05T06:57:54Z"},{"alias_kind":"pith_short_8","alias_value":"5CHE7BW2","created_at":"2026-07-05T06:57:54Z"}],"graph_snapshots":[{"event_id":"sha256:b7d35f14c494437668e365e9af0384d6117aad9d608cb491767a395c14470199","target":"graph","created_at":"2026-07-05T06:57:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2306.15350/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Nuclei detection and segmentation in hematoxylin and eosin-stained (H&E) tissue images are important clinical tasks and crucial for a wide range of applications. However, it is a challenging task due to nuclei variances in staining and size, overlapping boundaries, and nuclei clustering. While convolutional neural networks have been extensively used for this task, we explore the potential of Transformer-based networks in this domain. Therefore, we introduce a new method for automated instance segmentation of cell nuclei in digitized tissue samples using a deep learning architecture based on Vi","authors_text":"Barbara Gr\\\"unwald, Constantin Seibold, Fabian H\\\"orst, Giulia Baldini, Jan Egger, Jens Kleesiek, Jens Siveke, Julius Keyl, Lukas Heine, Moritz Rempe, Selma Ugurel","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2023-06-27T10:03:15Z","title":"CellViT: Vision Transformers for Precise Cell Segmentation and Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.15350","kind":"arxiv","version":2},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:8bb56f0d9ab552449d209a8caaf454bdf04f6b65d83c25a7b2653db796eee741","target":"record","created_at":"2026-07-05T06:57:54Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"2019579d8f54ba6a2e032fa3bcfe6aac74bf2f2b93ede5a4c89757dd39711ef8","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"eess.IV","submitted_at":"2023-06-27T10:03:15Z","title_canon_sha256":"2885bfa7f81fca25d4a627a098a4b9e15bc272bbf6224767e80e38ebffbc898b"},"schema_version":"1.0","source":{"id":"2306.15350","kind":"arxiv","version":2}},"canonical_sha256":"e88e4f86da094fe2dd6faffb749aa6110d69c08cd8cdb819909c11f81d6f546d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"e88e4f86da094fe2dd6faffb749aa6110d69c08cd8cdb819909c11f81d6f546d","first_computed_at":"2026-07-05T06:57:54.161122Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:57:54.161122Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"awsOFDT5EUEMCt3XcJu9/5lOiVcCOka/VXbrTbBYkVtO+L3elSBitq852YKWMZ+CAtUWCYWFXg8xyVXwProbAw==","signature_status":"signed_v1","signed_at":"2026-07-05T06:57:54.161621Z","signed_message":"canonical_sha256_bytes"},"source_id":"2306.15350","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:8bb56f0d9ab552449d209a8caaf454bdf04f6b65d83c25a7b2653db796eee741","sha256:b7d35f14c494437668e365e9af0384d6117aad9d608cb491767a395c14470199"],"state_sha256":"8610aceab75043e43c64b068a7b48a565243bc8ca8c4dfa7d40ed9b980dc217d"}