{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:5T2PE3XOKTLU3NICDH6W2DSJLL","short_pith_number":"pith:5T2PE3XO","canonical_record":{"source":{"id":"2605.14590","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T09:00:04Z","cross_cats_sorted":[],"title_canon_sha256":"6951f88e5aefd1fc5f9d4ea1c0c9d921d4d17a74978465a64839a6d5f0dd86b2","abstract_canon_sha256":"e06d174f850632fa44fd30d4dfbe5848610a825aebf1aac0b34e538f9286b740"},"schema_version":"1.0"},"canonical_sha256":"ecf4f26eee54d74db50219fd6d0e495af032e901604ccbda48fa437c4aab653d","source":{"kind":"arxiv","id":"2605.14590","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14590","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14590v1","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14590","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"pith_short_12","alias_value":"5T2PE3XOKTLU","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"5T2PE3XOKTLU3NIC","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"5T2PE3XO","created_at":"2026-05-18T12:33:37Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:5T2PE3XOKTLU3NICDH6W2DSJLL","target":"record","payload":{"canonical_record":{"source":{"id":"2605.14590","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T09:00:04Z","cross_cats_sorted":[],"title_canon_sha256":"6951f88e5aefd1fc5f9d4ea1c0c9d921d4d17a74978465a64839a6d5f0dd86b2","abstract_canon_sha256":"e06d174f850632fa44fd30d4dfbe5848610a825aebf1aac0b34e538f9286b740"},"schema_version":"1.0"},"canonical_sha256":"ecf4f26eee54d74db50219fd6d0e495af032e901604ccbda48fa437c4aab653d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:39:05.255930Z","signature_b64":"QGsIKc5gHD/gtPu2NGP7R5FRtUsiWOfF2pQ1/3/VdsdJJeMd/6jbGr/fVRbChYg4MQ3hDmGh6gwP/JBOCFpPDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ecf4f26eee54d74db50219fd6d0e495af032e901604ccbda48fa437c4aab653d","last_reissued_at":"2026-05-17T23:39:05.255251Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:39:05.255251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.14590","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"qbgNS6MyXGmFDttGnC952J8oYtWD5xPt0rfOYZiNwRqF5MXo/bo9pb2mYqq+OW4zW0yFpaMIjfCUbWlrPULRAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T02:32:35.434791Z"},"content_sha256":"68a33f38afb36a4efeb3c03f800e2af78d0186040f54fa8acd74fa08da72cf7b","schema_version":"1.0","event_id":"sha256:68a33f38afb36a4efeb3c03f800e2af78d0186040f54fa8acd74fa08da72cf7b"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:5T2PE3XOKTLU3NICDH6W2DSJLL","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology","license":"http://creativecommons.org/licenses/by/4.0/","headline":"FedStain lets sites share skewness and kurtosis of stain colors to train pathology models that generalize across institutions.","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Fengyi Zhang, Junya Zhang, Wenzhuo Sun","submitted_at":"2026-05-14T09:00:04Z","abstract_excerpt":"Robust whole-slide image (WSI) analysis under strict data-governance remains challenging due to substantial cross-institutional stain heterogeneity. Domain generalization (DG) mitigates these shifts but typically requires centralized data, conflicting with privacy regulations. Federated learning (FedL) provides a decentralized alternative; however, existing FedL and federated DG (FedDG) approaches rely almost exclusively on low-order statistics, assuming Gaussian-like stain distributions. In contrast, real-world staining processes often produce asymmetric, heavy-tailed color distributions due "},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"To our knowledge, FedStain is the first FedDG approach to explicitly model higher-order stain statistics, enabling robust cross-institutional deployment in computational pathology. FedStain yields consistent improvements, outperforming state-of-the-art FedL, DG, and FedDG baselines by up to +3.9% absolute accuracy.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That skewness and kurtosis, when exchanged as compact descriptors, sufficiently capture the dominant non-Gaussian stain variability and that the contrastive cross-site aggregation produces stain-invariant representations without relaxing privacy constraints.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"FedStain improves federated domain generalization in computational pathology by exchanging higher-order stain moments (skewness, kurtosis) to capture non-Gaussian variability, outperforming baselines by up to 3.9% accuracy.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"FedStain lets sites share skewness and kurtosis of stain colors to train pathology models that generalize across institutions.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"81f3f9931b70907dc7008d144109939f157b4b006ffdb3da4f2f9e2a210263e6"},"source":{"id":"2605.14590","kind":"arxiv","version":1},"verdict":{"id":"2f03f8ff-836f-4258-807e-2ade9aacf3b6","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-15T01:36:07.728944Z","strongest_claim":"To our knowledge, FedStain is the first FedDG approach to explicitly model higher-order stain statistics, enabling robust cross-institutional deployment in computational pathology. FedStain yields consistent improvements, outperforming state-of-the-art FedL, DG, and FedDG baselines by up to +3.9% absolute accuracy.","one_line_summary":"FedStain improves federated domain generalization in computational pathology by exchanging higher-order stain moments (skewness, kurtosis) to capture non-Gaussian variability, outperforming baselines by up to 3.9% accuracy.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That skewness and kurtosis, when exchanged as compact descriptors, sufficiently capture the dominant non-Gaussian stain variability and that the contrastive cross-site aggregation produces stain-invariant representations without relaxing privacy constraints.","pith_extraction_headline":"FedStain lets sites share skewness and kurtosis of stain colors to train pathology models that generalize across institutions."},"references":{"count":76,"sample":[{"doi":"","year":2017,"title":"A Survey on Deep Learning in Medical Image Analysis,","work_id":"30a9da38-3ce6-4b34-8400-52507b80cdcc","ref_index":1,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2018,"title":"Machine Learning Methods for Histopathological Image Analysis,","work_id":"b147eb21-6236-44e9-94e9-7afd02d0c722","ref_index":2,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":1996,"title":"Summary of the HIPAA Privacy Rule,","work_id":"20b311cb-737e-45aa-965e-90e379822879","ref_index":3,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2016,"title":"Regulation (EU) 2016/679: General Data Protection Regulation,","work_id":"2cabb7d2-6fc9-4872-84df-01d9a2751872","ref_index":4,"cited_arxiv_id":"","is_internal_anchor":false},{"doi":"","year":2019,"title":"Quantifying the Effects of Data Augmentation and Stain Color Normalization in Convolutional Neural Networks for Computational Pathology,","work_id":"d1378c34-76c7-47ac-a0b6-e83a3d6f6277","ref_index":5,"cited_arxiv_id":"","is_internal_anchor":false}],"resolved_work":76,"snapshot_sha256":"66bdba23564574b52a08e0f4c0f1f86975d487cd71b02a1c70d5192c36d36ea5","internal_anchors":0},"formal_canon":{"evidence_count":1,"snapshot_sha256":"9eb84f22fea6ae9b400ed4c71de60f13fe83c2018317ff77b944a3ee0485c82f"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"verdict_id":"2f03f8ff-836f-4258-807e-2ade9aacf3b6"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-17T23:39:05Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"l5kbkdyznoUPBYQr8D+KzBT/LJwMF4ADypyEEsCSdqacvppCDMLcfKTv0KljNuMEH7iTvBo929PX7WiahBOoDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-05-22T02:32:35.435714Z"},"content_sha256":"706e8b3b653d4960146dd1b416b0cad4fd440d6f71188671c7909dabb21ddc7f","schema_version":"1.0","event_id":"sha256:706e8b3b653d4960146dd1b416b0cad4fd440d6f71188671c7909dabb21ddc7f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/5T2PE3XOKTLU3NICDH6W2DSJLL/bundle.json","state_url":"https://pith.science/pith/5T2PE3XOKTLU3NICDH6W2DSJLL/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/5T2PE3XOKTLU3NICDH6W2DSJLL/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-05-22T02:32:35Z","links":{"resolver":"https://pith.science/pith/5T2PE3XOKTLU3NICDH6W2DSJLL","bundle":"https://pith.science/pith/5T2PE3XOKTLU3NICDH6W2DSJLL/bundle.json","state":"https://pith.science/pith/5T2PE3XOKTLU3NICDH6W2DSJLL/state.json","well_known_bundle":"https://pith.science/.well-known/pith/5T2PE3XOKTLU3NICDH6W2DSJLL/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:5T2PE3XOKTLU3NICDH6W2DSJLL","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":"e06d174f850632fa44fd30d4dfbe5848610a825aebf1aac0b34e538f9286b740","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T09:00:04Z","title_canon_sha256":"6951f88e5aefd1fc5f9d4ea1c0c9d921d4d17a74978465a64839a6d5f0dd86b2"},"schema_version":"1.0","source":{"id":"2605.14590","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.14590","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"arxiv_version","alias_value":"2605.14590v1","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.14590","created_at":"2026-05-17T23:39:05Z"},{"alias_kind":"pith_short_12","alias_value":"5T2PE3XOKTLU","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_16","alias_value":"5T2PE3XOKTLU3NIC","created_at":"2026-05-18T12:33:37Z"},{"alias_kind":"pith_short_8","alias_value":"5T2PE3XO","created_at":"2026-05-18T12:33:37Z"}],"graph_snapshots":[{"event_id":"sha256:706e8b3b653d4960146dd1b416b0cad4fd440d6f71188671c7909dabb21ddc7f","target":"graph","created_at":"2026-05-17T23:39:05Z","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":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"To our knowledge, FedStain is the first FedDG approach to explicitly model higher-order stain statistics, enabling robust cross-institutional deployment in computational pathology. FedStain yields consistent improvements, outperforming state-of-the-art FedL, DG, and FedDG baselines by up to +3.9% absolute accuracy."},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"That skewness and kurtosis, when exchanged as compact descriptors, sufficiently capture the dominant non-Gaussian stain variability and that the contrastive cross-site aggregation produces stain-invariant representations without relaxing privacy constraints."},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"FedStain improves federated domain generalization in computational pathology by exchanging higher-order stain moments (skewness, kurtosis) to capture non-Gaussian variability, outperforming baselines by up to 3.9% accuracy."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"FedStain lets sites share skewness and kurtosis of stain colors to train pathology models that generalize across institutions."}],"snapshot_sha256":"81f3f9931b70907dc7008d144109939f157b4b006ffdb3da4f2f9e2a210263e6"},"formal_canon":{"evidence_count":1,"snapshot_sha256":"9eb84f22fea6ae9b400ed4c71de60f13fe83c2018317ff77b944a3ee0485c82f"},"paper":{"abstract_excerpt":"Robust whole-slide image (WSI) analysis under strict data-governance remains challenging due to substantial cross-institutional stain heterogeneity. Domain generalization (DG) mitigates these shifts but typically requires centralized data, conflicting with privacy regulations. Federated learning (FedL) provides a decentralized alternative; however, existing FedL and federated DG (FedDG) approaches rely almost exclusively on low-order statistics, assuming Gaussian-like stain distributions. In contrast, real-world staining processes often produce asymmetric, heavy-tailed color distributions due ","authors_text":"Fengyi Zhang, Junya Zhang, Wenzhuo Sun","cross_cats":[],"headline":"FedStain lets sites share skewness and kurtosis of stain colors to train pathology models that generalize across institutions.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T09:00:04Z","title":"FedStain: Modeling Higher-Order Stain Statistics for Federated Domain Generalization in Computational Pathology"},"references":{"count":76,"internal_anchors":0,"resolved_work":76,"sample":[{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":1,"title":"A Survey on Deep Learning in Medical Image Analysis,","work_id":"30a9da38-3ce6-4b34-8400-52507b80cdcc","year":2017},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":2,"title":"Machine Learning Methods for Histopathological Image Analysis,","work_id":"b147eb21-6236-44e9-94e9-7afd02d0c722","year":2018},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":3,"title":"Summary of the HIPAA Privacy Rule,","work_id":"20b311cb-737e-45aa-965e-90e379822879","year":1996},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":4,"title":"Regulation (EU) 2016/679: General Data Protection Regulation,","work_id":"2cabb7d2-6fc9-4872-84df-01d9a2751872","year":2016},{"cited_arxiv_id":"","doi":"","is_internal_anchor":false,"ref_index":5,"title":"Quantifying the Effects of Data Augmentation and Stain Color Normalization in Convolutional Neural Networks for Computational Pathology,","work_id":"d1378c34-76c7-47ac-a0b6-e83a3d6f6277","year":2019}],"snapshot_sha256":"66bdba23564574b52a08e0f4c0f1f86975d487cd71b02a1c70d5192c36d36ea5"},"source":{"id":"2605.14590","kind":"arxiv","version":1},"verdict":{"created_at":"2026-05-15T01:36:07.728944Z","id":"2f03f8ff-836f-4258-807e-2ade9aacf3b6","model_set":{"reader":"grok-4.3"},"one_line_summary":"FedStain improves federated domain generalization in computational pathology by exchanging higher-order stain moments (skewness, kurtosis) to capture non-Gaussian variability, outperforming baselines by up to 3.9% accuracy.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"FedStain lets sites share skewness and kurtosis of stain colors to train pathology models that generalize across institutions.","strongest_claim":"To our knowledge, FedStain is the first FedDG approach to explicitly model higher-order stain statistics, enabling robust cross-institutional deployment in computational pathology. FedStain yields consistent improvements, outperforming state-of-the-art FedL, DG, and FedDG baselines by up to +3.9% absolute accuracy.","weakest_assumption":"That skewness and kurtosis, when exchanged as compact descriptors, sufficiently capture the dominant non-Gaussian stain variability and that the contrastive cross-site aggregation produces stain-invariant representations without relaxing privacy constraints."}},"verdict_id":"2f03f8ff-836f-4258-807e-2ade9aacf3b6"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:68a33f38afb36a4efeb3c03f800e2af78d0186040f54fa8acd74fa08da72cf7b","target":"record","created_at":"2026-05-17T23:39:05Z","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":"e06d174f850632fa44fd30d4dfbe5848610a825aebf1aac0b34e538f9286b740","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2026-05-14T09:00:04Z","title_canon_sha256":"6951f88e5aefd1fc5f9d4ea1c0c9d921d4d17a74978465a64839a6d5f0dd86b2"},"schema_version":"1.0","source":{"id":"2605.14590","kind":"arxiv","version":1}},"canonical_sha256":"ecf4f26eee54d74db50219fd6d0e495af032e901604ccbda48fa437c4aab653d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ecf4f26eee54d74db50219fd6d0e495af032e901604ccbda48fa437c4aab653d","first_computed_at":"2026-05-17T23:39:05.255251Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:39:05.255251Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"QGsIKc5gHD/gtPu2NGP7R5FRtUsiWOfF2pQ1/3/VdsdJJeMd/6jbGr/fVRbChYg4MQ3hDmGh6gwP/JBOCFpPDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:39:05.255930Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.14590","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:68a33f38afb36a4efeb3c03f800e2af78d0186040f54fa8acd74fa08da72cf7b","sha256:706e8b3b653d4960146dd1b416b0cad4fd440d6f71188671c7909dabb21ddc7f"],"state_sha256":"ab6c44928dccb1b58c8a149986a02a6c728ec601f2f1a97e6835be05fb8bb72b"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"BJxUtjddKhL5tpcH0TfBMNVAVQUGS2XgMXpo8C4MtLQpXFZVkoBZQVA1d68YHkbVENWouGXIk+UBDLYrKhz2Ag==","signed_message":"bundle_sha256_bytes","signed_at":"2026-05-22T02:32:35.439756Z","bundle_sha256":"c4047864c4ef578eca8d97949d72c90c0f2569fd7799a447b5c2388896096d80"}}