{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:2VPC3XORHLGIF6BGMLEJUE6D3F","short_pith_number":"pith:2VPC3XOR","schema_version":"1.0","canonical_sha256":"d55e2dddd13acc82f82662c89a13c3d94842ad17527af032a7531efca8c267dc","source":{"kind":"arxiv","id":"2409.20287","version":1},"attestation_state":"computed","paper":{"title":"Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Andreas Wirtz, Arjan Kuijper, Stefan Wesarg, Tillmann Rheude","submitted_at":"2024-09-30T13:43:00Z","abstract_excerpt":"Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understanding of the accurate decision-making process of a CNN is rather unknown. The research area of explainable artificial intelligence (xAI) primarily revolves around understanding and interpreting this black-box behavior. One way of interpreting a CNN is the use of class activation maps (CAMs) that represent heatmaps to indicate the importance of image areas for the prediction of the CNN. For classification tasks, a variet"},"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":"2409.20287","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"eess.IV","submitted_at":"2024-09-30T13:43:00Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"4716a8b069d4ee71dd849d31aac04179abd947407814cf1e7311cba089c2c8cb","abstract_canon_sha256":"b0f9068951044131a07ff3dc9af98f57cb9fdd5c5fcf5ca44dc7c21df311f406"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:13:41.364920Z","signature_b64":"9lYWUyIpOPiYuANeaFCV1RBh7ajkJ8wu03U3yXiUYIUpZqGO7jhISSmmJIsumdb6u9AZKBIpzn66Cz09a9q1Ag==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d55e2dddd13acc82f82662c89a13c3d94842ad17527af032a7531efca8c267dc","last_reissued_at":"2026-07-05T09:13:41.364529Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:13:41.364529Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Leveraging CAM Algorithms for Explaining Medical Semantic Segmentation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Andreas Wirtz, Arjan Kuijper, Stefan Wesarg, Tillmann Rheude","submitted_at":"2024-09-30T13:43:00Z","abstract_excerpt":"Convolutional neural networks (CNNs) achieve prevailing results in segmentation tasks nowadays and represent the state-of-the-art for image-based analysis. However, the understanding of the accurate decision-making process of a CNN is rather unknown. The research area of explainable artificial intelligence (xAI) primarily revolves around understanding and interpreting this black-box behavior. One way of interpreting a CNN is the use of class activation maps (CAMs) that represent heatmaps to indicate the importance of image areas for the prediction of the CNN. For classification tasks, a variet"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2409.20287","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/2409.20287/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":"2409.20287","created_at":"2026-07-05T09:13:41.364581+00:00"},{"alias_kind":"arxiv_version","alias_value":"2409.20287v1","created_at":"2026-07-05T09:13:41.364581+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2409.20287","created_at":"2026-07-05T09:13:41.364581+00:00"},{"alias_kind":"pith_short_12","alias_value":"2VPC3XORHLGI","created_at":"2026-07-05T09:13:41.364581+00:00"},{"alias_kind":"pith_short_16","alias_value":"2VPC3XORHLGIF6BG","created_at":"2026-07-05T09:13:41.364581+00:00"},{"alias_kind":"pith_short_8","alias_value":"2VPC3XOR","created_at":"2026-07-05T09:13:41.364581+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/2VPC3XORHLGIF6BGMLEJUE6D3F","json":"https://pith.science/pith/2VPC3XORHLGIF6BGMLEJUE6D3F.json","graph_json":"https://pith.science/api/pith-number/2VPC3XORHLGIF6BGMLEJUE6D3F/graph.json","events_json":"https://pith.science/api/pith-number/2VPC3XORHLGIF6BGMLEJUE6D3F/events.json","paper":"https://pith.science/paper/2VPC3XOR"},"agent_actions":{"view_html":"https://pith.science/pith/2VPC3XORHLGIF6BGMLEJUE6D3F","download_json":"https://pith.science/pith/2VPC3XORHLGIF6BGMLEJUE6D3F.json","view_paper":"https://pith.science/paper/2VPC3XOR","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2409.20287&json=true","fetch_graph":"https://pith.science/api/pith-number/2VPC3XORHLGIF6BGMLEJUE6D3F/graph.json","fetch_events":"https://pith.science/api/pith-number/2VPC3XORHLGIF6BGMLEJUE6D3F/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2VPC3XORHLGIF6BGMLEJUE6D3F/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2VPC3XORHLGIF6BGMLEJUE6D3F/action/storage_attestation","attest_author":"https://pith.science/pith/2VPC3XORHLGIF6BGMLEJUE6D3F/action/author_attestation","sign_citation":"https://pith.science/pith/2VPC3XORHLGIF6BGMLEJUE6D3F/action/citation_signature","submit_replication":"https://pith.science/pith/2VPC3XORHLGIF6BGMLEJUE6D3F/action/replication_record"}},"created_at":"2026-07-05T09:13:41.364581+00:00","updated_at":"2026-07-05T09:13:41.364581+00:00"}