{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2017:V4BVWLWGEATEF7WLTYRKR4ZOVD","short_pith_number":"pith:V4BVWLWG","canonical_record":{"source":{"id":"1711.08760","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-11-23T16:25:29Z","cross_cats_sorted":[],"title_canon_sha256":"64504fafd3710fc570a972646be89d4485e9f2139b66924a85babd9398cc956d","abstract_canon_sha256":"9b2d944f2ca3121c7def810a213052ce3efb1e7612020368e5aa2612ea0ddfdd"},"schema_version":"1.0"},"canonical_sha256":"af035b2ec6202642fecb9e22a8f32ea8e6e7a8536b273418642c3c114d7ea1fa","source":{"kind":"arxiv","id":"1711.08760","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.08760","created_at":"2026-05-18T00:29:44Z"},{"alias_kind":"arxiv_version","alias_value":"1711.08760v1","created_at":"2026-05-18T00:29:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08760","created_at":"2026-05-18T00:29:44Z"},{"alias_kind":"pith_short_12","alias_value":"V4BVWLWGEATE","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"V4BVWLWGEATEF7WL","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"V4BVWLWG","created_at":"2026-05-18T12:31:49Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2017:V4BVWLWGEATEF7WLTYRKR4ZOVD","target":"record","payload":{"canonical_record":{"source":{"id":"1711.08760","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-11-23T16:25:29Z","cross_cats_sorted":[],"title_canon_sha256":"64504fafd3710fc570a972646be89d4485e9f2139b66924a85babd9398cc956d","abstract_canon_sha256":"9b2d944f2ca3121c7def810a213052ce3efb1e7612020368e5aa2612ea0ddfdd"},"schema_version":"1.0"},"canonical_sha256":"af035b2ec6202642fecb9e22a8f32ea8e6e7a8536b273418642c3c114d7ea1fa","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:29:44.175170Z","signature_b64":"Q+4IESMFpuTIOVb3FrUWl298CNr3siETBuqqPh6+zQ7blCmbHyg/0WCCuuLe+nhQxIlYvfpMHzmcjlrBH7SwCg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"af035b2ec6202642fecb9e22a8f32ea8e6e7a8536b273418642c3c114d7ea1fa","last_reissued_at":"2026-05-18T00:29:44.174595Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:29:44.174595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1711.08760","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":"2026-05-18T00:29:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"tbOVQCsrJmhf/EwrBq0woqMTGWW0o4sTHOlJl2QYwcq3wqJ1/iRJEfNn/S3c0F4EB6OaQlZA643Paj0LS4BfAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T18:07:22.184282Z"},"content_sha256":"4941243b3fe3187a93da16516373090ee117f0a45f4a913bb462579054316552","schema_version":"1.0","event_id":"sha256:4941243b3fe3187a93da16516373090ee117f0a45f4a913bb462579054316552"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2017:V4BVWLWGEATEF7WLTYRKR4ZOVD","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar","submitted_at":"2017-11-23T16:25:29Z","abstract_excerpt":"Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantita"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08760","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":""},"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":"2026-05-18T00:29:44Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"ZipoNjCFwtcOxaOK3AOvGBkisx4TBLjEkCfEmqK1Ryvl+FBWogHw57UEFimT7xFq+dYmfGEF8GxJT31nmTKIAQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-05T18:07:22.184947Z"},"content_sha256":"f06dd4c500447ed4d8e05eff8f803f92a92af4a29caf2a1d306dd58d4cb235ce","schema_version":"1.0","event_id":"sha256:f06dd4c500447ed4d8e05eff8f803f92a92af4a29caf2a1d306dd58d4cb235ce"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/V4BVWLWGEATEF7WLTYRKR4ZOVD/bundle.json","state_url":"https://pith.science/pith/V4BVWLWGEATEF7WLTYRKR4ZOVD/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/V4BVWLWGEATEF7WLTYRKR4ZOVD/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":"2026-06-05T18:07:22Z","links":{"resolver":"https://pith.science/pith/V4BVWLWGEATEF7WLTYRKR4ZOVD","bundle":"https://pith.science/pith/V4BVWLWGEATEF7WLTYRKR4ZOVD/bundle.json","state":"https://pith.science/pith/V4BVWLWGEATEF7WLTYRKR4ZOVD/state.json","well_known_bundle":"https://pith.science/.well-known/pith/V4BVWLWGEATEF7WLTYRKR4ZOVD/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2017:V4BVWLWGEATEF7WLTYRKR4ZOVD","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":"9b2d944f2ca3121c7def810a213052ce3efb1e7612020368e5aa2612ea0ddfdd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-11-23T16:25:29Z","title_canon_sha256":"64504fafd3710fc570a972646be89d4485e9f2139b66924a85babd9398cc956d"},"schema_version":"1.0","source":{"id":"1711.08760","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1711.08760","created_at":"2026-05-18T00:29:44Z"},{"alias_kind":"arxiv_version","alias_value":"1711.08760v1","created_at":"2026-05-18T00:29:44Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1711.08760","created_at":"2026-05-18T00:29:44Z"},{"alias_kind":"pith_short_12","alias_value":"V4BVWLWGEATE","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_16","alias_value":"V4BVWLWGEATEF7WL","created_at":"2026-05-18T12:31:49Z"},{"alias_kind":"pith_short_8","alias_value":"V4BVWLWG","created_at":"2026-05-18T12:31:49Z"}],"graph_snapshots":[{"event_id":"sha256:f06dd4c500447ed4d8e05eff8f803f92a92af4a29caf2a1d306dd58d4cb235ce","target":"graph","created_at":"2026-05-18T00:29:44Z","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"},"paper":{"abstract_excerpt":"Chest X-ray is one of the most accessible medical imaging technique for diagnosis of multiple diseases. With the availability of ChestX-ray14, which is a massive dataset of chest X-ray images and provides annotations for 14 thoracic diseases; it is possible to train Deep Convolutional Neural Networks (DCNN) to build Computer Aided Diagnosis (CAD) systems. In this work, we experiment a set of deep learning models and present a cascaded deep neural network that can diagnose all 14 pathologies better than the baseline and is competitive with other published methods. Our work provides the quantita","authors_text":"Monika Grewal, Muktabh Mayank Srivastava, Pulkit Kumar","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-11-23T16:25:29Z","title":"Boosted Cascaded Convnets for Multilabel Classification of Thoracic Diseases in Chest Radiographs"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1711.08760","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":"sha256:4941243b3fe3187a93da16516373090ee117f0a45f4a913bb462579054316552","target":"record","created_at":"2026-05-18T00:29:44Z","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":"9b2d944f2ca3121c7def810a213052ce3efb1e7612020368e5aa2612ea0ddfdd","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"cs.CV","submitted_at":"2017-11-23T16:25:29Z","title_canon_sha256":"64504fafd3710fc570a972646be89d4485e9f2139b66924a85babd9398cc956d"},"schema_version":"1.0","source":{"id":"1711.08760","kind":"arxiv","version":1}},"canonical_sha256":"af035b2ec6202642fecb9e22a8f32ea8e6e7a8536b273418642c3c114d7ea1fa","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"af035b2ec6202642fecb9e22a8f32ea8e6e7a8536b273418642c3c114d7ea1fa","first_computed_at":"2026-05-18T00:29:44.174595Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-18T00:29:44.174595Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Q+4IESMFpuTIOVb3FrUWl298CNr3siETBuqqPh6+zQ7blCmbHyg/0WCCuuLe+nhQxIlYvfpMHzmcjlrBH7SwCg==","signature_status":"signed_v1","signed_at":"2026-05-18T00:29:44.175170Z","signed_message":"canonical_sha256_bytes"},"source_id":"1711.08760","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:4941243b3fe3187a93da16516373090ee117f0a45f4a913bb462579054316552","sha256:f06dd4c500447ed4d8e05eff8f803f92a92af4a29caf2a1d306dd58d4cb235ce"],"state_sha256":"66ee0723b846f3fe2aee060061c260cc96435ce0cc2575639efa1a427eb076e7"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Fec60yBTfs+UY805sUxIftj43IKf61RQ/WAKM7iAyvkve433nN54G7ZL7Qwd1CwMb0qC7flwPsXTSMLgwsUPDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-05T18:07:22.188379Z","bundle_sha256":"275ffee4b1df331b507dc42b1c276dfb3eb4d961197d65d7885ab23946845ad4"}}