{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:MMX2Q6W5CBJIPK7QRPPXZIZQ7E","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":"e76ac9df53b86db042a985cf413f857dcd8685a50e200a17a5adcb647eb4f42a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-04-05T05:47:56Z","title_canon_sha256":"fc5e37dcfc89c67d5f9fa26205ba1cf802e9a3f460d268fc9effefb3b6739202"},"schema_version":"1.0","source":{"id":"2504.04066","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2504.04066","created_at":"2026-07-05T10:45:21Z"},{"alias_kind":"arxiv_version","alias_value":"2504.04066v1","created_at":"2026-07-05T10:45:21Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2504.04066","created_at":"2026-07-05T10:45:21Z"},{"alias_kind":"pith_short_12","alias_value":"MMX2Q6W5CBJI","created_at":"2026-07-05T10:45:21Z"},{"alias_kind":"pith_short_16","alias_value":"MMX2Q6W5CBJIPK7Q","created_at":"2026-07-05T10:45:21Z"},{"alias_kind":"pith_short_8","alias_value":"MMX2Q6W5","created_at":"2026-07-05T10:45:21Z"}],"graph_snapshots":[{"event_id":"sha256:cda7097273158b625eb5ae699eac132bfdf5c23608915c355dd0c13a90da8fff","target":"graph","created_at":"2026-07-05T10:45:21Z","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/2504.04066/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Convolutional neural networks like U-Net excel in medical image segmentation, while attention mechanisms and KAN enhance feature extraction. Meta's SAM 2 uses Vision Transformers for prompt-based segmentation without fine-tuning. However, biases in these models impact generalization with limited data. In this study, we systematically evaluate and compare the performance of three CNN-based models, i.e., U-Net, Attention U-Net, and U-KAN, and one transformer-based model, i.e., SAM 2 for segmenting femur bone structures in MRI scan. The dataset comprises 11,164 MRI scans with detailed annotations","authors_text":"Anning Tian, Jeongkyu Lee, Mengyuan Liu, Mozhi Shen, Tianchou Gong, Xinmeng Wu, Yixiao Chen","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-04-05T05:47:56Z","title":"Performance Analysis of Deep Learning Models for Femur Segmentation in MRI Scan"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2504.04066","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:c1471e713b04de523abd8e088af706afabbed548ffdd186b7c8234b5395d6518","target":"record","created_at":"2026-07-05T10:45:21Z","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":"e76ac9df53b86db042a985cf413f857dcd8685a50e200a17a5adcb647eb4f42a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2025-04-05T05:47:56Z","title_canon_sha256":"fc5e37dcfc89c67d5f9fa26205ba1cf802e9a3f460d268fc9effefb3b6739202"},"schema_version":"1.0","source":{"id":"2504.04066","kind":"arxiv","version":1}},"canonical_sha256":"632fa87add105287abf08bdf7ca330f9303babefad6c0ebda9d4a1190e6cc715","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"632fa87add105287abf08bdf7ca330f9303babefad6c0ebda9d4a1190e6cc715","first_computed_at":"2026-07-05T10:45:21.453280Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T10:45:21.453280Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UyHBdQeQJ/oi6ZttgaxEMFa2Df7/RIKgMPKR9dWfvyPP8uO7T2YbPBw1REYhZ/04AmmVPynCYbXb03gXQ2+rAA==","signature_status":"signed_v1","signed_at":"2026-07-05T10:45:21.453780Z","signed_message":"canonical_sha256_bytes"},"source_id":"2504.04066","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:c1471e713b04de523abd8e088af706afabbed548ffdd186b7c8234b5395d6518","sha256:cda7097273158b625eb5ae699eac132bfdf5c23608915c355dd0c13a90da8fff"],"state_sha256":"d88d191e4c13f7dfee4df6bf0e1f7d122ebf59515906ae69c048e4da22c63984"}