{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:3C7S3ZYXZ5A5G3MN222YOKCMFS","short_pith_number":"pith:3C7S3ZYX","schema_version":"1.0","canonical_sha256":"d8bf2de717cf41d36d8dd6b587284c2cb52e0a102de05f2460e47746019eb59f","source":{"kind":"arxiv","id":"2406.05074","version":2},"attestation_state":"computed","paper":{"title":"Hibou: A Family of Foundational Vision Transformers for Pathology","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexey Pchelnikov, Dmitry Nechaev, Ekaterina Ivanova","submitted_at":"2024-06-07T16:45:53Z","abstract_excerpt":"Pathology, the microscopic examination of diseased tissue, is critical for diagnosing various medical conditions, particularly cancers. Traditional methods are labor-intensive and prone to human error. Digital pathology, which converts glass slides into high-resolution digital images for analysis by computer algorithms, revolutionizes the field by enhancing diagnostic accuracy, consistency, and efficiency through automated image analysis and large-scale data processing. Foundational transformer pretraining is crucial for developing robust, generalizable models as it enables learning from vast "},"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":"2406.05074","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-06-07T16:45:53Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"ef5356815f7a6485ef8b608fd833ad59e5d85c3a41699309443e19e79620d1af","abstract_canon_sha256":"d847d200edda4fe63be91f4174265009a88296cf2bcb123e7557f6a7cf80ca98"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:57:05.055090Z","signature_b64":"udS7udodKEDXC5OdXSsEIADHQuUWMQhJ6S2dnRZccR3Yjr5i95VU2h3f1lnvXTE1jwuFDvtbLItUkxlK2yGGDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d8bf2de717cf41d36d8dd6b587284c2cb52e0a102de05f2460e47746019eb59f","last_reissued_at":"2026-07-05T08:57:05.054558Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:57:05.054558Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Hibou: A Family of Foundational Vision Transformers for Pathology","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Alexey Pchelnikov, Dmitry Nechaev, Ekaterina Ivanova","submitted_at":"2024-06-07T16:45:53Z","abstract_excerpt":"Pathology, the microscopic examination of diseased tissue, is critical for diagnosing various medical conditions, particularly cancers. Traditional methods are labor-intensive and prone to human error. Digital pathology, which converts glass slides into high-resolution digital images for analysis by computer algorithms, revolutionizes the field by enhancing diagnostic accuracy, consistency, and efficiency through automated image analysis and large-scale data processing. Foundational transformer pretraining is crucial for developing robust, generalizable models as it enables learning from vast "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.05074","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/2406.05074/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":"2406.05074","created_at":"2026-07-05T08:57:05.054626+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.05074v2","created_at":"2026-07-05T08:57:05.054626+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.05074","created_at":"2026-07-05T08:57:05.054626+00:00"},{"alias_kind":"pith_short_12","alias_value":"3C7S3ZYXZ5A5","created_at":"2026-07-05T08:57:05.054626+00:00"},{"alias_kind":"pith_short_16","alias_value":"3C7S3ZYXZ5A5G3MN","created_at":"2026-07-05T08:57:05.054626+00:00"},{"alias_kind":"pith_short_8","alias_value":"3C7S3ZYX","created_at":"2026-07-05T08:57:05.054626+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":10,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06983","citing_title":"DaX: Learning General Pathology Representations Across Scales","ref_index":71,"is_internal_anchor":false},{"citing_arxiv_id":"2606.07368","citing_title":"Mitosis Detection in the Wild: Multi-Tumor and Context-Aware Generalization in the MIDOG 2025 Challenge","ref_index":85,"is_internal_anchor":false},{"citing_arxiv_id":"2607.00802","citing_title":"CellPrior-Net: Prior-Guided Nuclei Detection and Classification for H&E Whole-Slide Images","ref_index":11,"is_internal_anchor":false},{"citing_arxiv_id":"2606.28697","citing_title":"Mitigating Batch Effects in Histopathology via Language-Mediated Robust Embedding Generation","ref_index":52,"is_internal_anchor":false},{"citing_arxiv_id":"2606.30020","citing_title":"Uncertainty Estimation in Pathology Foundation Models via Deep Mutual Learning","ref_index":27,"is_internal_anchor":false},{"citing_arxiv_id":"2605.25764","citing_title":"Benchmarking Pathology Foundation Models for Spatial Domain Understanding","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2410.06723","citing_title":"Evaluating Computational Pathology Foundation Models for Prostate Cancer Grading under Distribution Shifts","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2602.22347","citing_title":"Enabling clinical use of foundation models for computational pathology","ref_index":6,"is_internal_anchor":false},{"citing_arxiv_id":"2605.08276","citing_title":"Beyond ViT Tokens: Masked-Diffusion Pretrained Convolutional Pathology Foundation Model for Cell-Level Dense Prediction","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.10362","citing_title":"CellDX AI Autopilot: Agent-Guided Training and Deployment of Pathology Classifiers","ref_index":15,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS","json":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS.json","graph_json":"https://pith.science/api/pith-number/3C7S3ZYXZ5A5G3MN222YOKCMFS/graph.json","events_json":"https://pith.science/api/pith-number/3C7S3ZYXZ5A5G3MN222YOKCMFS/events.json","paper":"https://pith.science/paper/3C7S3ZYX"},"agent_actions":{"view_html":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS","download_json":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS.json","view_paper":"https://pith.science/paper/3C7S3ZYX","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.05074&json=true","fetch_graph":"https://pith.science/api/pith-number/3C7S3ZYXZ5A5G3MN222YOKCMFS/graph.json","fetch_events":"https://pith.science/api/pith-number/3C7S3ZYXZ5A5G3MN222YOKCMFS/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS/action/timestamp_anchor","attest_storage":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS/action/storage_attestation","attest_author":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS/action/author_attestation","sign_citation":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS/action/citation_signature","submit_replication":"https://pith.science/pith/3C7S3ZYXZ5A5G3MN222YOKCMFS/action/replication_record"}},"created_at":"2026-07-05T08:57:05.054626+00:00","updated_at":"2026-07-05T08:57:05.054626+00:00"}