{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:XN4MIF6IONXUK2NZOR6NGWALQD","short_pith_number":"pith:XN4MIF6I","canonical_record":{"source":{"id":"2210.02998","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-10-06T15:38:02Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"17f14588e14a8d776e2d08fd60045da53a377d842ac7ecde6f110ad873a67dc9","abstract_canon_sha256":"716a7eb595b613bab5ea9e4eca6b8ac8e0e570fc6bc02f589665bff1c5c3a26e"},"schema_version":"1.0"},"canonical_sha256":"bb78c417c8736f4569b9747cd3580b80cacbf84ad3d4a5e8b229feac1add8c1b","source":{"kind":"arxiv","id":"2210.02998","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.02998","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"arxiv_version","alias_value":"2210.02998v3","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02998","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"pith_short_12","alias_value":"XN4MIF6IONXU","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"pith_short_16","alias_value":"XN4MIF6IONXUK2NZ","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"pith_short_8","alias_value":"XN4MIF6I","created_at":"2026-07-05T07:27:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:XN4MIF6IONXUK2NZOR6NGWALQD","target":"record","payload":{"canonical_record":{"source":{"id":"2210.02998","kind":"arxiv","version":3},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-10-06T15:38:02Z","cross_cats_sorted":["cs.CV","cs.LG"],"title_canon_sha256":"17f14588e14a8d776e2d08fd60045da53a377d842ac7ecde6f110ad873a67dc9","abstract_canon_sha256":"716a7eb595b613bab5ea9e4eca6b8ac8e0e570fc6bc02f589665bff1c5c3a26e"},"schema_version":"1.0"},"canonical_sha256":"bb78c417c8736f4569b9747cd3580b80cacbf84ad3d4a5e8b229feac1add8c1b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:27:41.468583Z","signature_b64":"arKO9mkH0RhUI8/PlBAGbSnBnUplF8zM59Sp6iPr9dl+3ax/OEOoY5H4h8veWZ0f0HX8Vh0OuojjnI3/9hFfBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bb78c417c8736f4569b9747cd3580b80cacbf84ad3d4a5e8b229feac1add8c1b","last_reissued_at":"2026-07-05T07:27:41.468086Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:27:41.468086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2210.02998","source_version":3,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:27:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"9M9iinkeH20X/X7pv60y3cvPFwnrUDxlRNtCB9WZmcEJO8Ug938hLx46+SSlFWGUF3eVlUFD8ojYt5C1AQeVBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T16:50:00.253669Z"},"content_sha256":"1cfbb4ef6d8af87faf2f6821fafbac74690990b4bc4a36d4ba1629bbee3df774","schema_version":"1.0","event_id":"sha256:1cfbb4ef6d8af87faf2f6821fafbac74690990b4bc4a36d4ba1629bbee3df774"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:XN4MIF6IONXUK2NZOR6NGWALQD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","cs.LG"],"primary_cat":"eess.IV","authors_text":"Anwarul Hasan, Md. Iqbal Hossain, Md. Kawsar Ahmed, Mohammad Zunaed, S. M. Jawwad Hossain, Taufiq Hasan","submitted_at":"2022-10-06T15:38:02Z","abstract_excerpt":"Objective: Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classification and localization from chest X-ray images. It is known that different thoracic disease lesions are more likely to occur in specific anatomical regions compared to others. This article aims to incorporate this disease and region-dependent prior probability distribution within a deep learning framework. Methods: We present the ThoraX-PriorNet, a novel attention-based CNN mo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02998","kind":"arxiv","version":3},"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/2210.02998/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":"2026-07-05T07:27:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"/0GojR/hfMX0PJxQ5oTtL3dFq7LWEn1gTcYuAmpdSTBRm6whQXzoGO5ny7YBfmIGOvDXCggvCOfdfq4GN4DnBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-22T16:50:00.254041Z"},"content_sha256":"168833bc4b49ab450d1a41ac1046e7760fd5ad6a8a8c65d8cd25ebe74c3b3cef","schema_version":"1.0","event_id":"sha256:168833bc4b49ab450d1a41ac1046e7760fd5ad6a8a8c65d8cd25ebe74c3b3cef"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XN4MIF6IONXUK2NZOR6NGWALQD/bundle.json","state_url":"https://pith.science/pith/XN4MIF6IONXUK2NZOR6NGWALQD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XN4MIF6IONXUK2NZOR6NGWALQD/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":"2026-07-22T16:50:00Z","links":{"resolver":"https://pith.science/pith/XN4MIF6IONXUK2NZOR6NGWALQD","bundle":"https://pith.science/pith/XN4MIF6IONXUK2NZOR6NGWALQD/bundle.json","state":"https://pith.science/pith/XN4MIF6IONXUK2NZOR6NGWALQD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XN4MIF6IONXUK2NZOR6NGWALQD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:XN4MIF6IONXUK2NZOR6NGWALQD","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":"716a7eb595b613bab5ea9e4eca6b8ac8e0e570fc6bc02f589665bff1c5c3a26e","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-10-06T15:38:02Z","title_canon_sha256":"17f14588e14a8d776e2d08fd60045da53a377d842ac7ecde6f110ad873a67dc9"},"schema_version":"1.0","source":{"id":"2210.02998","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2210.02998","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"arxiv_version","alias_value":"2210.02998v3","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2210.02998","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"pith_short_12","alias_value":"XN4MIF6IONXU","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"pith_short_16","alias_value":"XN4MIF6IONXUK2NZ","created_at":"2026-07-05T07:27:41Z"},{"alias_kind":"pith_short_8","alias_value":"XN4MIF6I","created_at":"2026-07-05T07:27:41Z"}],"graph_snapshots":[{"event_id":"sha256:168833bc4b49ab450d1a41ac1046e7760fd5ad6a8a8c65d8cd25ebe74c3b3cef","target":"graph","created_at":"2026-07-05T07:27:41Z","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/2210.02998/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Objective: Computer-aided disease diagnosis and prognosis based on medical images is a rapidly emerging field. Many Convolutional Neural Network (CNN) architectures have been developed by researchers for disease classification and localization from chest X-ray images. It is known that different thoracic disease lesions are more likely to occur in specific anatomical regions compared to others. This article aims to incorporate this disease and region-dependent prior probability distribution within a deep learning framework. Methods: We present the ThoraX-PriorNet, a novel attention-based CNN mo","authors_text":"Anwarul Hasan, Md. Iqbal Hossain, Md. Kawsar Ahmed, Mohammad Zunaed, S. M. Jawwad Hossain, Taufiq Hasan","cross_cats":["cs.CV","cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-10-06T15:38:02Z","title":"ThoraX-PriorNet: A Novel Attention-Based Architecture Using Anatomical Prior Probability Maps for Thoracic Disease Classification"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2210.02998","kind":"arxiv","version":3},"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":"sha256:1cfbb4ef6d8af87faf2f6821fafbac74690990b4bc4a36d4ba1629bbee3df774","target":"record","created_at":"2026-07-05T07:27:41Z","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":"716a7eb595b613bab5ea9e4eca6b8ac8e0e570fc6bc02f589665bff1c5c3a26e","cross_cats_sorted":["cs.CV","cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2022-10-06T15:38:02Z","title_canon_sha256":"17f14588e14a8d776e2d08fd60045da53a377d842ac7ecde6f110ad873a67dc9"},"schema_version":"1.0","source":{"id":"2210.02998","kind":"arxiv","version":3}},"canonical_sha256":"bb78c417c8736f4569b9747cd3580b80cacbf84ad3d4a5e8b229feac1add8c1b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bb78c417c8736f4569b9747cd3580b80cacbf84ad3d4a5e8b229feac1add8c1b","first_computed_at":"2026-07-05T07:27:41.468086Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:27:41.468086Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"arKO9mkH0RhUI8/PlBAGbSnBnUplF8zM59Sp6iPr9dl+3ax/OEOoY5H4h8veWZ0f0HX8Vh0OuojjnI3/9hFfBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T07:27:41.468583Z","signed_message":"canonical_sha256_bytes"},"source_id":"2210.02998","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:1cfbb4ef6d8af87faf2f6821fafbac74690990b4bc4a36d4ba1629bbee3df774","sha256:168833bc4b49ab450d1a41ac1046e7760fd5ad6a8a8c65d8cd25ebe74c3b3cef"],"state_sha256":"ea7987b4dce026ef299bdf5aa02fd9127f488e16a6383e675ceacc4e87286bda"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sGAFnGrAf+975bz0LwEikFP1dWBbFcGpfYGd29nwAoKQc48Vn2L+5nfZCzPFB6DYywZtEviMt/1PRBEqQS+8Bw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-22T16:50:00.256248Z","bundle_sha256":"5e2309a9c0e464c187d47143170d6c0667c73e06a1083885b753f461b8f6842b"}}