{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:IC2LV7ZMJTPLYNZ47IRNIUERYQ","short_pith_number":"pith:IC2LV7ZM","schema_version":"1.0","canonical_sha256":"40b4baff2c4cdebc373cfa22d45091c408b7cdb0c6862a3024b9467569e4c809","source":{"kind":"arxiv","id":"2009.11009","version":1},"attestation_state":"computed","paper":{"title":"Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Anat Shalmon, Arnaldo Mayer, Eli Konen, Gavriel Habib, Miri Sklair-Levy, Nahum Kiryati, Osnat Halshtok Neiman, Renata Faermann Weidenfeld, Yael Yagil","submitted_at":"2020-09-23T09:08:24Z","abstract_excerpt":"Mammography and ultrasound are extensively used by radiologists as complementary modalities to achieve better performance in breast cancer diagnosis. However, existing computer-aided diagnosis (CAD) systems for the breast are generally based on a single modality. In this work, we propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. We present various approaches and show a consistent improvement in performance when utilizing both modalities. The proposed approach is based on a GoogleNet architecture, fine-tuned for o"},"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":"2009.11009","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-09-23T09:08:24Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"5816d00039594530cebd49b1d2b6525a64072b4f5ec0877f4eae211bb38bd2d9","abstract_canon_sha256":"33c013df4dd9ae02a9f29c34e5d7197e42d5d1abac4fc3e1b7ccf8f8472fb207"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:37:35.520309Z","signature_b64":"WPH3WwI/6PUMt0SwjnFwUVd3J7mY00AS1igu8BzHsUUQwY5mFa+Iv3v/z0NB/hP1SteE3lAIzra/7rrmTADyCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"40b4baff2c4cdebc373cfa22d45091c408b7cdb0c6862a3024b9467569e4c809","last_reissued_at":"2026-07-05T01:37:35.519930Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:37:35.519930Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Automatic Breast Lesion Classification by Joint Neural Analysis of Mammography and Ultrasound","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Anat Shalmon, Arnaldo Mayer, Eli Konen, Gavriel Habib, Miri Sklair-Levy, Nahum Kiryati, Osnat Halshtok Neiman, Renata Faermann Weidenfeld, Yael Yagil","submitted_at":"2020-09-23T09:08:24Z","abstract_excerpt":"Mammography and ultrasound are extensively used by radiologists as complementary modalities to achieve better performance in breast cancer diagnosis. However, existing computer-aided diagnosis (CAD) systems for the breast are generally based on a single modality. In this work, we propose a deep-learning based method for classifying breast cancer lesions from their respective mammography and ultrasound images. We present various approaches and show a consistent improvement in performance when utilizing both modalities. The proposed approach is based on a GoogleNet architecture, fine-tuned for o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2009.11009","kind":"arxiv","version":1},"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/2009.11009/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":"2009.11009","created_at":"2026-07-05T01:37:35.519986+00:00"},{"alias_kind":"arxiv_version","alias_value":"2009.11009v1","created_at":"2026-07-05T01:37:35.519986+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2009.11009","created_at":"2026-07-05T01:37:35.519986+00:00"},{"alias_kind":"pith_short_12","alias_value":"IC2LV7ZMJTPL","created_at":"2026-07-05T01:37:35.519986+00:00"},{"alias_kind":"pith_short_16","alias_value":"IC2LV7ZMJTPLYNZ4","created_at":"2026-07-05T01:37:35.519986+00:00"},{"alias_kind":"pith_short_8","alias_value":"IC2LV7ZM","created_at":"2026-07-05T01:37:35.519986+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ","json":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ.json","graph_json":"https://pith.science/api/pith-number/IC2LV7ZMJTPLYNZ47IRNIUERYQ/graph.json","events_json":"https://pith.science/api/pith-number/IC2LV7ZMJTPLYNZ47IRNIUERYQ/events.json","paper":"https://pith.science/paper/IC2LV7ZM"},"agent_actions":{"view_html":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ","download_json":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ.json","view_paper":"https://pith.science/paper/IC2LV7ZM","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2009.11009&json=true","fetch_graph":"https://pith.science/api/pith-number/IC2LV7ZMJTPLYNZ47IRNIUERYQ/graph.json","fetch_events":"https://pith.science/api/pith-number/IC2LV7ZMJTPLYNZ47IRNIUERYQ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ/action/storage_attestation","attest_author":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ/action/author_attestation","sign_citation":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ/action/citation_signature","submit_replication":"https://pith.science/pith/IC2LV7ZMJTPLYNZ47IRNIUERYQ/action/replication_record"}},"created_at":"2026-07-05T01:37:35.519986+00:00","updated_at":"2026-07-05T01:37:35.519986+00:00"}