{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:MTSIAVMSTKYP646Q5VZHELBPGX","short_pith_number":"pith:MTSIAVMS","schema_version":"1.0","canonical_sha256":"64e48055929ab0ff73d0ed72722c2f35cde0301edeecf2110448c23c8bdc8d0c","source":{"kind":"arxiv","id":"2503.01306","version":1},"attestation_state":"computed","paper":{"title":"From Claims to Evidence: A Unified Framework and Critical Analysis of CNN vs. Transformer vs. Mamba in Medical Image Segmentation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Anselm W. Stark, Christoph Graeni, George CM. Siontis, Giovanni Baj, Habib Zaidi, Isaac Shiri, Mauricio Reyes, Pooya Mohammadi Kazaj, Waldo Valenzuela, Yazdan Salimi","submitted_at":"2025-03-03T08:44:51Z","abstract_excerpt":"While numerous architectures for medical image segmentation have been proposed, achieving competitive performance with state-of-the-art models networks such as nnUNet, still leave room for further innovation. In this work, we introduce nnUZoo, an open source benchmarking framework built upon nnUNet, which incorporates various deep learning architectures, including CNNs, Transformers, and Mamba-based models. Using this framework, we provide a fair comparison to demystify performance claims across different medical image segmentation tasks. Additionally, in an effort to enrich the benchmarking, "},"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":"2503.01306","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2025-03-03T08:44:51Z","cross_cats_sorted":["cs.AI","cs.CV","physics.med-ph"],"title_canon_sha256":"768ea5ae882520c663c335b76b53f6ee5bc3012cc17d0d72a3e19b8976d712f5","abstract_canon_sha256":"3b2b43329c60023915142fbe02041acced466562817c31be5566fcee41b3c400"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:23:04.245362Z","signature_b64":"k84iq96d+lNWSMhP3/RHIrAlYeYkko/8fwODHe02efvI82zExMOW7BET5iI6agR++pYwxroe5lfOBdikrNp7Bg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"64e48055929ab0ff73d0ed72722c2f35cde0301edeecf2110448c23c8bdc8d0c","last_reissued_at":"2026-07-05T10:23:04.244513Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:23:04.244513Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"From Claims to Evidence: A Unified Framework and Critical Analysis of CNN vs. Transformer vs. Mamba in Medical Image Segmentation","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["cs.AI","cs.CV","physics.med-ph"],"primary_cat":"eess.IV","authors_text":"Anselm W. Stark, Christoph Graeni, George CM. Siontis, Giovanni Baj, Habib Zaidi, Isaac Shiri, Mauricio Reyes, Pooya Mohammadi Kazaj, Waldo Valenzuela, Yazdan Salimi","submitted_at":"2025-03-03T08:44:51Z","abstract_excerpt":"While numerous architectures for medical image segmentation have been proposed, achieving competitive performance with state-of-the-art models networks such as nnUNet, still leave room for further innovation. In this work, we introduce nnUZoo, an open source benchmarking framework built upon nnUNet, which incorporates various deep learning architectures, including CNNs, Transformers, and Mamba-based models. Using this framework, we provide a fair comparison to demystify performance claims across different medical image segmentation tasks. Additionally, in an effort to enrich the benchmarking, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2503.01306","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/2503.01306/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":"2503.01306","created_at":"2026-07-05T10:23:04.244627+00:00"},{"alias_kind":"arxiv_version","alias_value":"2503.01306v1","created_at":"2026-07-05T10:23:04.244627+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2503.01306","created_at":"2026-07-05T10:23:04.244627+00:00"},{"alias_kind":"pith_short_12","alias_value":"MTSIAVMSTKYP","created_at":"2026-07-05T10:23:04.244627+00:00"},{"alias_kind":"pith_short_16","alias_value":"MTSIAVMSTKYP646Q","created_at":"2026-07-05T10:23:04.244627+00:00"},{"alias_kind":"pith_short_8","alias_value":"MTSIAVMS","created_at":"2026-07-05T10:23:04.244627+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2604.10702","citing_title":"Backbone-Conditional Behavior of Modality Gating in Multi-Modal Prostate MRI Segmentation: A 5-Fold Cross-Validation and Gate Mechanism Analysis","ref_index":22,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX","json":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX.json","graph_json":"https://pith.science/api/pith-number/MTSIAVMSTKYP646Q5VZHELBPGX/graph.json","events_json":"https://pith.science/api/pith-number/MTSIAVMSTKYP646Q5VZHELBPGX/events.json","paper":"https://pith.science/paper/MTSIAVMS"},"agent_actions":{"view_html":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX","download_json":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX.json","view_paper":"https://pith.science/paper/MTSIAVMS","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2503.01306&json=true","fetch_graph":"https://pith.science/api/pith-number/MTSIAVMSTKYP646Q5VZHELBPGX/graph.json","fetch_events":"https://pith.science/api/pith-number/MTSIAVMSTKYP646Q5VZHELBPGX/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX/action/storage_attestation","attest_author":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX/action/author_attestation","sign_citation":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX/action/citation_signature","submit_replication":"https://pith.science/pith/MTSIAVMSTKYP646Q5VZHELBPGX/action/replication_record"}},"created_at":"2026-07-05T10:23:04.244627+00:00","updated_at":"2026-07-05T10:23:04.244627+00:00"}