{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:WSYBSPB2JGGTNUUZ6M7H3FV5YG","short_pith_number":"pith:WSYBSPB2","schema_version":"1.0","canonical_sha256":"b4b0193c3a498d36d299f33e7d96bdc19a2cb53ac2691f31d16a6d75a9a98636","source":{"kind":"arxiv","id":"2206.05575","version":5},"attestation_state":"computed","paper":{"title":"MammoFL: Mammographic Breast Density Estimation using Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.DC","cs.LG"],"primary_cat":"eess.IV","authors_text":"Angelina Heyler, Aprupa Alahari, Celine Vachon, Christopher Scott, Despina Kontos, Emily F. Conant, Keshava Katti, Michael Sanborn, Pratik Chaudhari, Ramya Muthukrishnan, Sarthak Pati, Spyridon Bakas, Stacey Winham, Walter Mankowski","submitted_at":"2022-06-11T17:38:09Z","abstract_excerpt":"In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were separately trained on algorithm-generated labels to perform segmentation of the breast and dense tissue from these images and subsequently calculate breast percent density (PD). The networks were trained with federated learning and compared to three non-federated baselines, one "},"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":"2206.05575","kind":"arxiv","version":5},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-06-11T17:38:09Z","cross_cats_sorted":["cs.CV","cs.DC","cs.LG"],"title_canon_sha256":"1aa5e9a5b3e7de740a7953802e9313ae71f92d11676124fa252fdb6f851d6daa","abstract_canon_sha256":"49ccd211782b9a16468ea1f17f23b5896fd4dc49f3457de8c36026377264b865"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:23:53.998681Z","signature_b64":"JYvDIt0yAFuKCEj5iKvBCTczhA7kiivxTs2//f81X2kecpvrvvycZMvqOgxVE2VbpMMDlokpM5KxQROKYN9VDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b4b0193c3a498d36d299f33e7d96bdc19a2cb53ac2691f31d16a6d75a9a98636","last_reissued_at":"2026-07-05T07:23:53.998176Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:23:53.998176Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MammoFL: Mammographic Breast Density Estimation using Federated Learning","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.DC","cs.LG"],"primary_cat":"eess.IV","authors_text":"Angelina Heyler, Aprupa Alahari, Celine Vachon, Christopher Scott, Despina Kontos, Emily F. Conant, Keshava Katti, Michael Sanborn, Pratik Chaudhari, Ramya Muthukrishnan, Sarthak Pati, Spyridon Bakas, Stacey Winham, Walter Mankowski","submitted_at":"2022-06-11T17:38:09Z","abstract_excerpt":"In this study, we automate quantitative mammographic breast density estimation with neural networks and show that this tool is a strong use case for federated learning on multi-institutional datasets. Our dataset included bilateral CC-view and MLO-view mammographic images from two separate institutions. Two U-Nets were separately trained on algorithm-generated labels to perform segmentation of the breast and dense tissue from these images and subsequently calculate breast percent density (PD). The networks were trained with federated learning and compared to three non-federated baselines, one "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2206.05575","kind":"arxiv","version":5},"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/2206.05575/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":"2206.05575","created_at":"2026-07-05T07:23:53.998236+00:00"},{"alias_kind":"arxiv_version","alias_value":"2206.05575v5","created_at":"2026-07-05T07:23:53.998236+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2206.05575","created_at":"2026-07-05T07:23:53.998236+00:00"},{"alias_kind":"pith_short_12","alias_value":"WSYBSPB2JGGT","created_at":"2026-07-05T07:23:53.998236+00:00"},{"alias_kind":"pith_short_16","alias_value":"WSYBSPB2JGGTNUUZ","created_at":"2026-07-05T07:23:53.998236+00:00"},{"alias_kind":"pith_short_8","alias_value":"WSYBSPB2","created_at":"2026-07-05T07:23:53.998236+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09137","citing_title":"Evaluating Federated Learning approaches for mammography under breast density heterogeneity","ref_index":50,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG","json":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG.json","graph_json":"https://pith.science/api/pith-number/WSYBSPB2JGGTNUUZ6M7H3FV5YG/graph.json","events_json":"https://pith.science/api/pith-number/WSYBSPB2JGGTNUUZ6M7H3FV5YG/events.json","paper":"https://pith.science/paper/WSYBSPB2"},"agent_actions":{"view_html":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG","download_json":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG.json","view_paper":"https://pith.science/paper/WSYBSPB2","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2206.05575&json=true","fetch_graph":"https://pith.science/api/pith-number/WSYBSPB2JGGTNUUZ6M7H3FV5YG/graph.json","fetch_events":"https://pith.science/api/pith-number/WSYBSPB2JGGTNUUZ6M7H3FV5YG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG/action/storage_attestation","attest_author":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG/action/author_attestation","sign_citation":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG/action/citation_signature","submit_replication":"https://pith.science/pith/WSYBSPB2JGGTNUUZ6M7H3FV5YG/action/replication_record"}},"created_at":"2026-07-05T07:23:53.998236+00:00","updated_at":"2026-07-05T07:23:53.998236+00:00"}