{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:DGD5T6HKZ46VQRTLVSHG4UDEQP","short_pith_number":"pith:DGD5T6HK","canonical_record":{"source":{"id":"2204.08324","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-18T13:52:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0080aed11c33357573177782e089e5ddea3ab7f04dd9c6ce116256ecdd1e22f0","abstract_canon_sha256":"e71dbf0a3f497a0ea3ac6b977f5c0dac3163680851dd51b2e98c5284d7bf7b60"},"schema_version":"1.0"},"canonical_sha256":"1987d9f8eacf3d58466bac8e6e506483d8c64556dbd7839756623b38d20e8e28","source":{"kind":"arxiv","id":"2204.08324","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.08324","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"arxiv_version","alias_value":"2204.08324v2","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.08324","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"pith_short_12","alias_value":"DGD5T6HKZ46V","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"pith_short_16","alias_value":"DGD5T6HKZ46VQRTL","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"pith_short_8","alias_value":"DGD5T6HK","created_at":"2026-07-05T04:16:19Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:DGD5T6HKZ46VQRTLVSHG4UDEQP","target":"record","payload":{"canonical_record":{"source":{"id":"2204.08324","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-18T13:52:06Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"0080aed11c33357573177782e089e5ddea3ab7f04dd9c6ce116256ecdd1e22f0","abstract_canon_sha256":"e71dbf0a3f497a0ea3ac6b977f5c0dac3163680851dd51b2e98c5284d7bf7b60"},"schema_version":"1.0"},"canonical_sha256":"1987d9f8eacf3d58466bac8e6e506483d8c64556dbd7839756623b38d20e8e28","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:16:19.568954Z","signature_b64":"R+gfjfTIXg6jma3pRX/UH7+fu9M7rwkJrf6OeBOU8JlVKGhlSnle7JLVxfaC51lK+02REiOKAUHKp0CWq/hsCA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"1987d9f8eacf3d58466bac8e6e506483d8c64556dbd7839756623b38d20e8e28","last_reissued_at":"2026-07-05T04:16:19.568535Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:16:19.568535Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2204.08324","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:16:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Gp5Bh2wqDkN5jULC/JDAn6xmjxUi1yQG9AEhHXpbNPsORf7ZWguntr7vrmhsJC2tvzDlpm2vzAiIyhgFxSfABQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T11:47:50.719554Z"},"content_sha256":"a23387fb53ee3a6e4334f696a9f2208755165247c1b123408f731f8cb8e34073","schema_version":"1.0","event_id":"sha256:a23387fb53ee3a6e4334f696a9f2208755165247c1b123408f731f8cb8e34073"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:DGD5T6HKZ46VQRTLVSHG4UDEQP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Hierarchical Optimal Transport for Comparing Histopathology Datasets","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.CV","authors_text":"Anna Yeaton, David Alvarez-Melis, Grace Huynh, Rahul G. Krishnan, Rebecca Mieloszyk","submitted_at":"2022-04-18T13:52:06Z","abstract_excerpt":"Scarcity of labeled histopathology data limits the applicability of deep learning methods to under-profiled cancer types and labels. Transfer learning allows researchers to overcome the limitations of small datasets by pre-training machine learning models on larger datasets similar to the small target dataset. However, similarity between datasets is often determined heuristically. In this paper, we propose a principled notion of distance between histopathology datasets based on a hierarchical generalization of optimal transport distances. Our method does not require any training, is agnostic t"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.08324","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/2204.08324/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:16:19Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"HkVycGJ3lHOx0KHF7aeHVnMNKecfZ4zmrYiMOhf9+uIXa8HuqPo6rM5HICML1T79POOVYxRAQ6HCGsKu14lCAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-11T11:47:50.720373Z"},"content_sha256":"12cbf01dc3ba38f49f03b094c1caedc1119ba85d6d53ce1d08bb403b0b72f8bc","schema_version":"1.0","event_id":"sha256:12cbf01dc3ba38f49f03b094c1caedc1119ba85d6d53ce1d08bb403b0b72f8bc"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/DGD5T6HKZ46VQRTLVSHG4UDEQP/bundle.json","state_url":"https://pith.science/pith/DGD5T6HKZ46VQRTLVSHG4UDEQP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/DGD5T6HKZ46VQRTLVSHG4UDEQP/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":"2026-08-11T11:47:50Z","links":{"resolver":"https://pith.science/pith/DGD5T6HKZ46VQRTLVSHG4UDEQP","bundle":"https://pith.science/pith/DGD5T6HKZ46VQRTLVSHG4UDEQP/bundle.json","state":"https://pith.science/pith/DGD5T6HKZ46VQRTLVSHG4UDEQP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/DGD5T6HKZ46VQRTLVSHG4UDEQP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:DGD5T6HKZ46VQRTLVSHG4UDEQP","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":"e71dbf0a3f497a0ea3ac6b977f5c0dac3163680851dd51b2e98c5284d7bf7b60","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-18T13:52:06Z","title_canon_sha256":"0080aed11c33357573177782e089e5ddea3ab7f04dd9c6ce116256ecdd1e22f0"},"schema_version":"1.0","source":{"id":"2204.08324","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2204.08324","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"arxiv_version","alias_value":"2204.08324v2","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2204.08324","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"pith_short_12","alias_value":"DGD5T6HKZ46V","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"pith_short_16","alias_value":"DGD5T6HKZ46VQRTL","created_at":"2026-07-05T04:16:19Z"},{"alias_kind":"pith_short_8","alias_value":"DGD5T6HK","created_at":"2026-07-05T04:16:19Z"}],"graph_snapshots":[{"event_id":"sha256:12cbf01dc3ba38f49f03b094c1caedc1119ba85d6d53ce1d08bb403b0b72f8bc","target":"graph","created_at":"2026-07-05T04:16:19Z","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/2204.08324/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Scarcity of labeled histopathology data limits the applicability of deep learning methods to under-profiled cancer types and labels. Transfer learning allows researchers to overcome the limitations of small datasets by pre-training machine learning models on larger datasets similar to the small target dataset. However, similarity between datasets is often determined heuristically. In this paper, we propose a principled notion of distance between histopathology datasets based on a hierarchical generalization of optimal transport distances. Our method does not require any training, is agnostic t","authors_text":"Anna Yeaton, David Alvarez-Melis, Grace Huynh, Rahul G. Krishnan, Rebecca Mieloszyk","cross_cats":["cs.AI"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-18T13:52:06Z","title":"Hierarchical Optimal Transport for Comparing Histopathology Datasets"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2204.08324","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:a23387fb53ee3a6e4334f696a9f2208755165247c1b123408f731f8cb8e34073","target":"record","created_at":"2026-07-05T04:16:19Z","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":"e71dbf0a3f497a0ea3ac6b977f5c0dac3163680851dd51b2e98c5284d7bf7b60","cross_cats_sorted":["cs.AI"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2022-04-18T13:52:06Z","title_canon_sha256":"0080aed11c33357573177782e089e5ddea3ab7f04dd9c6ce116256ecdd1e22f0"},"schema_version":"1.0","source":{"id":"2204.08324","kind":"arxiv","version":2}},"canonical_sha256":"1987d9f8eacf3d58466bac8e6e506483d8c64556dbd7839756623b38d20e8e28","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"1987d9f8eacf3d58466bac8e6e506483d8c64556dbd7839756623b38d20e8e28","first_computed_at":"2026-07-05T04:16:19.568535Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:16:19.568535Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"R+gfjfTIXg6jma3pRX/UH7+fu9M7rwkJrf6OeBOU8JlVKGhlSnle7JLVxfaC51lK+02REiOKAUHKp0CWq/hsCA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:16:19.568954Z","signed_message":"canonical_sha256_bytes"},"source_id":"2204.08324","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:a23387fb53ee3a6e4334f696a9f2208755165247c1b123408f731f8cb8e34073","sha256:12cbf01dc3ba38f49f03b094c1caedc1119ba85d6d53ce1d08bb403b0b72f8bc"],"state_sha256":"b67cba530373aa77116aac488df42df912f4b7363cc7256def9ea186e345d957"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iF7nv6R+2/JY7AT9WqOK/Vn63MEejLmTcrGSon2iYdTlotLWwP9sSLHJ2T6+kNvPMtmH0C3BKs7N0Ory7raVDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-11T11:47:50.727761Z","bundle_sha256":"5367483e6e460fb6e4d1a8ee9774f55e632e112fc46924b5759c167b2a5c33be"}}