{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:KFA4XI6DXJ5ZQ7XAERPVPLCI6O","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":"4a14ce145102978fa9a13b79b6e1b08c27f77c9c01ecc410e7d1c062b4b2b628","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-05T11:03:15Z","title_canon_sha256":"f58f42201cd8558769a2e066c77366c362aa2e94811a292f40036bb4d3ffede6"},"schema_version":"1.0","source":{"id":"2509.05004","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2509.05004","created_at":"2026-07-05T12:05:34Z"},{"alias_kind":"arxiv_version","alias_value":"2509.05004v1","created_at":"2026-07-05T12:05:34Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2509.05004","created_at":"2026-07-05T12:05:34Z"},{"alias_kind":"pith_short_12","alias_value":"KFA4XI6DXJ5Z","created_at":"2026-07-05T12:05:34Z"},{"alias_kind":"pith_short_16","alias_value":"KFA4XI6DXJ5ZQ7XA","created_at":"2026-07-05T12:05:34Z"},{"alias_kind":"pith_short_8","alias_value":"KFA4XI6D","created_at":"2026-07-05T12:05:34Z"}],"graph_snapshots":[{"event_id":"sha256:d4f77de924a84b0c00a76337e9297db02ae272637a30eb5348ce534111be0f34","target":"graph","created_at":"2026-07-05T12:05:34Z","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/2509.05004/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Breast cancer remains a leading cause of cancer-related mortality among women worldwide. Ultrasound imaging, widely used due to its safety and cost-effectiveness, plays a key role in early detection, especially in patients with dense breast tissue. This paper presents a comprehensive study on the application of machine learning and deep learning techniques for breast cancer classification using ultrasound images. Using datasets such as BUSI, BUS-BRA, and BrEaST-Lesions USG, we evaluate classical machine learning models (SVM, KNN) and deep convolutional neural networks (ResNet-18, EfficientNet-","authors_text":"Mohammad Abbadi, Shadi Atalla, Wathiq Mansoor, Yassine Himeur","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-05T11:03:15Z","title":"Interpretable Deep Transfer Learning for Breast Ultrasound Cancer Detection: A Multi-Dataset Study"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2509.05004","kind":"arxiv","version":1},"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":"sha256:233c5321236e596acac3d8ec6ac15faabf9a817755670c606fe904b63a4e350d","target":"record","created_at":"2026-07-05T12:05:34Z","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":"4a14ce145102978fa9a13b79b6e1b08c27f77c9c01ecc410e7d1c062b4b2b628","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2025-09-05T11:03:15Z","title_canon_sha256":"f58f42201cd8558769a2e066c77366c362aa2e94811a292f40036bb4d3ffede6"},"schema_version":"1.0","source":{"id":"2509.05004","kind":"arxiv","version":1}},"canonical_sha256":"5141cba3c3ba7b987ee0245f57ac48f3a85c7e97fbe7d0e92d74477fff3ef403","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"5141cba3c3ba7b987ee0245f57ac48f3a85c7e97fbe7d0e92d74477fff3ef403","first_computed_at":"2026-07-05T12:05:34.325495Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T12:05:34.325495Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HYgHTj8W/05Y4Ha8/eZnqtgsHQ/4y/CF4cS5tvp7uAWIwHxXS3fZZdmd8zGm0PicCJZZ/TOycPOI6KdIs+mQBg==","signature_status":"signed_v1","signed_at":"2026-07-05T12:05:34.326023Z","signed_message":"canonical_sha256_bytes"},"source_id":"2509.05004","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:233c5321236e596acac3d8ec6ac15faabf9a817755670c606fe904b63a4e350d","sha256:d4f77de924a84b0c00a76337e9297db02ae272637a30eb5348ce534111be0f34"],"state_sha256":"2ba8a3aecc0e4dbabde28996d77cef06ff0b78b21bada5943ae115be9a97768a"}