{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:WYHDLEKDDDURYBLOKSZAQKE3AV","short_pith_number":"pith:WYHDLEKD","canonical_record":{"source":{"id":"2405.01636","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-02T18:00:25Z","cross_cats_sorted":[],"title_canon_sha256":"d14bc6860fd98bd72dd6fd6c70c2afd44675df521f41522c2908acb8431b34b7","abstract_canon_sha256":"b904a0b5a2394d17d6e927b3e10c7c2e14c4544b928caa4f6176bfa08f5e785e"},"schema_version":"1.0"},"canonical_sha256":"b60e35914318e91c056e54b208289b055fbe6661ba1e4ee7cbf06e47eb93ad8d","source":{"kind":"arxiv","id":"2405.01636","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.01636","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"arxiv_version","alias_value":"2405.01636v1","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.01636","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"pith_short_12","alias_value":"WYHDLEKDDDUR","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"pith_short_16","alias_value":"WYHDLEKDDDURYBLO","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"pith_short_8","alias_value":"WYHDLEKD","created_at":"2026-07-05T08:14:59Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:WYHDLEKDDDURYBLOKSZAQKE3AV","target":"record","payload":{"canonical_record":{"source":{"id":"2405.01636","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-02T18:00:25Z","cross_cats_sorted":[],"title_canon_sha256":"d14bc6860fd98bd72dd6fd6c70c2afd44675df521f41522c2908acb8431b34b7","abstract_canon_sha256":"b904a0b5a2394d17d6e927b3e10c7c2e14c4544b928caa4f6176bfa08f5e785e"},"schema_version":"1.0"},"canonical_sha256":"b60e35914318e91c056e54b208289b055fbe6661ba1e4ee7cbf06e47eb93ad8d","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:14:59.945013Z","signature_b64":"852f6DtstnwHl9yd6DQHWs5clrZW0RArKpQeuURwS4CF2I/0YpK3Tv+Npdg6PC+FutUeCUfgmGiyR4PXSHD0AQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"b60e35914318e91c056e54b208289b055fbe6661ba1e4ee7cbf06e47eb93ad8d","last_reissued_at":"2026-07-05T08:14:59.944576Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:14:59.944576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2405.01636","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:14:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"zPwv4UabztcYnUWs1E1omEjweDC9y+daOKHZ0wjWRy+pv2NiClMXaSE05AB1HjStg4/7chjuiLy9sYZ6aqaBCw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T10:09:39.004663Z"},"content_sha256":"98e4fd53f1aef20105f0209945c3ae39f8e9252e6baac4e83dcbeae4072697d3","schema_version":"1.0","event_id":"sha256:98e4fd53f1aef20105f0209945c3ae39f8e9252e6baac4e83dcbeae4072697d3"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:WYHDLEKDDDURYBLOKSZAQKE3AV","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Chun-Wei Tsai, Olga Kurasova, Rokas Gipi\\v{s}kis","submitted_at":"2024-05-02T18:00:25Z","abstract_excerpt":"Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were ext"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.01636","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/2405.01636/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T08:14:59Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"c3JDd+7WjhRdtMseBZRAbWu0g4Y8vW+O6HPwN6dfVqc1A9q+TIJ+5BZOTwFm9nhYaklfg5v39rGup3mUQVFkAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T10:09:39.005050Z"},"content_sha256":"0eaa3e555b5dc22b56e6e6d8288c70ae7c19f6ac2b8cf6d0c7d62973fb4b302b","schema_version":"1.0","event_id":"sha256:0eaa3e555b5dc22b56e6e6d8288c70ae7c19f6ac2b8cf6d0c7d62973fb4b302b"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/WYHDLEKDDDURYBLOKSZAQKE3AV/bundle.json","state_url":"https://pith.science/pith/WYHDLEKDDDURYBLOKSZAQKE3AV/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/WYHDLEKDDDURYBLOKSZAQKE3AV/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-07-07T10:09:39Z","links":{"resolver":"https://pith.science/pith/WYHDLEKDDDURYBLOKSZAQKE3AV","bundle":"https://pith.science/pith/WYHDLEKDDDURYBLOKSZAQKE3AV/bundle.json","state":"https://pith.science/pith/WYHDLEKDDDURYBLOKSZAQKE3AV/state.json","well_known_bundle":"https://pith.science/.well-known/pith/WYHDLEKDDDURYBLOKSZAQKE3AV/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:WYHDLEKDDDURYBLOKSZAQKE3AV","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":"b904a0b5a2394d17d6e927b3e10c7c2e14c4544b928caa4f6176bfa08f5e785e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-02T18:00:25Z","title_canon_sha256":"d14bc6860fd98bd72dd6fd6c70c2afd44675df521f41522c2908acb8431b34b7"},"schema_version":"1.0","source":{"id":"2405.01636","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2405.01636","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"arxiv_version","alias_value":"2405.01636v1","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2405.01636","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"pith_short_12","alias_value":"WYHDLEKDDDUR","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"pith_short_16","alias_value":"WYHDLEKDDDURYBLO","created_at":"2026-07-05T08:14:59Z"},{"alias_kind":"pith_short_8","alias_value":"WYHDLEKD","created_at":"2026-07-05T08:14:59Z"}],"graph_snapshots":[{"event_id":"sha256:0eaa3e555b5dc22b56e6e6d8288c70ae7c19f6ac2b8cf6d0c7d62973fb4b302b","target":"graph","created_at":"2026-07-05T08:14:59Z","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/2405.01636/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Artificial Intelligence (XAI) has found numerous applications in computer vision. While image classification-based explainability techniques have garnered significant attention, their counterparts in semantic segmentation have been relatively neglected. Given the prevalent use of image segmentation, ranging from medical to industrial deployments, these techniques warrant a systematic look. In this paper, we present the first comprehensive survey on XAI in semantic image segmentation. This work focuses on techniques that were either specifically introduced for dense prediction tasks or were ext","authors_text":"Chun-Wei Tsai, Olga Kurasova, Rokas Gipi\\v{s}kis","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-02T18:00:25Z","title":"Explainable AI (XAI) in Image Segmentation in Medicine, Industry, and Beyond: A Survey"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2405.01636","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":"sha256:98e4fd53f1aef20105f0209945c3ae39f8e9252e6baac4e83dcbeae4072697d3","target":"record","created_at":"2026-07-05T08:14:59Z","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":"b904a0b5a2394d17d6e927b3e10c7c2e14c4544b928caa4f6176bfa08f5e785e","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2024-05-02T18:00:25Z","title_canon_sha256":"d14bc6860fd98bd72dd6fd6c70c2afd44675df521f41522c2908acb8431b34b7"},"schema_version":"1.0","source":{"id":"2405.01636","kind":"arxiv","version":1}},"canonical_sha256":"b60e35914318e91c056e54b208289b055fbe6661ba1e4ee7cbf06e47eb93ad8d","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"b60e35914318e91c056e54b208289b055fbe6661ba1e4ee7cbf06e47eb93ad8d","first_computed_at":"2026-07-05T08:14:59.944576Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T08:14:59.944576Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"852f6DtstnwHl9yd6DQHWs5clrZW0RArKpQeuURwS4CF2I/0YpK3Tv+Npdg6PC+FutUeCUfgmGiyR4PXSHD0AQ==","signature_status":"signed_v1","signed_at":"2026-07-05T08:14:59.945013Z","signed_message":"canonical_sha256_bytes"},"source_id":"2405.01636","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:98e4fd53f1aef20105f0209945c3ae39f8e9252e6baac4e83dcbeae4072697d3","sha256:0eaa3e555b5dc22b56e6e6d8288c70ae7c19f6ac2b8cf6d0c7d62973fb4b302b"],"state_sha256":"a59353620809814b0ba7ab48e2770e2ca2b57cbad784490ac21ebac20d9ec7d2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hGslatmryhMrq/ekPuB2lbClMPJd6RRGNOSDh0qjyp7Oyjst7oIWBv8h+zkcE9QLTuUbl9TctUAIegysg7xsDA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T10:09:39.006993Z","bundle_sha256":"854478aa22a67d65746193a76c1050b7beb863e8b8ac3b3ec069d81fbdea92ed"}}