{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:QA4SGL7VDPFGIDCGZDD4JLLYMQ","short_pith_number":"pith:QA4SGL7V","canonical_record":{"source":{"id":"2007.00748","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-01T20:48:35Z","cross_cats_sorted":[],"title_canon_sha256":"e388d3b51507eb466f79d3430ecf0c442a838b531b85aa512d098d3e38e15f6a","abstract_canon_sha256":"c208cf80f222d470091eba204dbfd97a1a0144643ba59350fb84e0dcd21caf50"},"schema_version":"1.0"},"canonical_sha256":"8039232ff51bca640c46c8c7c4ad7864318d3f407fa9ce0942cfb144e59035d0","source":{"kind":"arxiv","id":"2007.00748","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.00748","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"arxiv_version","alias_value":"2007.00748v1","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.00748","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"pith_short_12","alias_value":"QA4SGL7VDPFG","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"pith_short_16","alias_value":"QA4SGL7VDPFGIDCG","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"pith_short_8","alias_value":"QA4SGL7V","created_at":"2026-07-05T01:15:40Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:QA4SGL7VDPFGIDCGZDD4JLLYMQ","target":"record","payload":{"canonical_record":{"source":{"id":"2007.00748","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-01T20:48:35Z","cross_cats_sorted":[],"title_canon_sha256":"e388d3b51507eb466f79d3430ecf0c442a838b531b85aa512d098d3e38e15f6a","abstract_canon_sha256":"c208cf80f222d470091eba204dbfd97a1a0144643ba59350fb84e0dcd21caf50"},"schema_version":"1.0"},"canonical_sha256":"8039232ff51bca640c46c8c7c4ad7864318d3f407fa9ce0942cfb144e59035d0","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:15:40.847700Z","signature_b64":"yqWg8T6hjxHxO3mk7pxWOmfyLnMdr+xHjYaR7LhLjEEJMQ+Z/arDB8Amwmhmm3OQH5IxPoMhcqw/Qs0bX8p+Aw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"8039232ff51bca640c46c8c7c4ad7864318d3f407fa9ce0942cfb144e59035d0","last_reissued_at":"2026-07-05T01:15:40.847259Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:15:40.847259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.00748","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":"2026-07-05T01:15:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RG/DMsJjx85Mjq1r9YjBDNQ5cqmb+NZYfnz+xIaXSdh2ZdGqjxhb5WzE9NbV0HkFc7Hgri+eVtomqlYn6l8DDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T04:50:43.831625Z"},"content_sha256":"43f0b1e39c4fe091bf2d55f852b4beb7ca2e10b1c62562987819fec9ee03de01","schema_version":"1.0","event_id":"sha256:43f0b1e39c4fe091bf2d55f852b4beb7ca2e10b1c62562987819fec9ee03de01"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:QA4SGL7VDPFGIDCGZDD4JLLYMQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Mariia Dobko, Oles Dobosevych, Ostap Viniavskyi","submitted_at":"2020-07-01T20:48:35Z","abstract_excerpt":"Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that are expensive to get and often require domain expertise, especially in medical imaging. Weakly supervised semantic segmentation helps to overcome these issues and also provides explainable deep learning models. In this paper, we propose a novel approach to the semantic segmentation of medical chest X-ray images with only image-level class labels as supervision. We improve the disease localization accuracy by combinin"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.00748","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/2007.00748/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":"2026-07-05T01:15:40Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"X05lqEhcTN1A95QdX19wVBrqcJ2miOET+hy0ZH3eYhLZsKlff0hHYR0EOKzO5lhlcAXD1TNWdyH+POnl8ZB3Aw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-22T04:50:43.832137Z"},"content_sha256":"827b7faaf1101d190fbbbfedbac6d7efcf7b70cf12952468ffea59b46cf333d5","schema_version":"1.0","event_id":"sha256:827b7faaf1101d190fbbbfedbac6d7efcf7b70cf12952468ffea59b46cf333d5"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/QA4SGL7VDPFGIDCGZDD4JLLYMQ/bundle.json","state_url":"https://pith.science/pith/QA4SGL7VDPFGIDCGZDD4JLLYMQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/QA4SGL7VDPFGIDCGZDD4JLLYMQ/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":"2026-08-22T04:50:43Z","links":{"resolver":"https://pith.science/pith/QA4SGL7VDPFGIDCGZDD4JLLYMQ","bundle":"https://pith.science/pith/QA4SGL7VDPFGIDCGZDD4JLLYMQ/bundle.json","state":"https://pith.science/pith/QA4SGL7VDPFGIDCGZDD4JLLYMQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/QA4SGL7VDPFGIDCGZDD4JLLYMQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:QA4SGL7VDPFGIDCGZDD4JLLYMQ","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":"c208cf80f222d470091eba204dbfd97a1a0144643ba59350fb84e0dcd21caf50","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-01T20:48:35Z","title_canon_sha256":"e388d3b51507eb466f79d3430ecf0c442a838b531b85aa512d098d3e38e15f6a"},"schema_version":"1.0","source":{"id":"2007.00748","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.00748","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"arxiv_version","alias_value":"2007.00748v1","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.00748","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"pith_short_12","alias_value":"QA4SGL7VDPFG","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"pith_short_16","alias_value":"QA4SGL7VDPFGIDCG","created_at":"2026-07-05T01:15:40Z"},{"alias_kind":"pith_short_8","alias_value":"QA4SGL7V","created_at":"2026-07-05T01:15:40Z"}],"graph_snapshots":[{"event_id":"sha256:827b7faaf1101d190fbbbfedbac6d7efcf7b70cf12952468ffea59b46cf333d5","target":"graph","created_at":"2026-07-05T01:15:40Z","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/2007.00748/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Deep Convolutional Neural Networks have proven effective in solving the task of semantic segmentation. However, their efficiency heavily relies on the pixel-level annotations that are expensive to get and often require domain expertise, especially in medical imaging. Weakly supervised semantic segmentation helps to overcome these issues and also provides explainable deep learning models. In this paper, we propose a novel approach to the semantic segmentation of medical chest X-ray images with only image-level class labels as supervision. We improve the disease localization accuracy by combinin","authors_text":"Mariia Dobko, Oles Dobosevych, Ostap Viniavskyi","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-01T20:48:35Z","title":"Weakly-Supervised Segmentation for Disease Localization in Chest X-Ray Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.00748","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":"sha256:43f0b1e39c4fe091bf2d55f852b4beb7ca2e10b1c62562987819fec9ee03de01","target":"record","created_at":"2026-07-05T01:15:40Z","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":"c208cf80f222d470091eba204dbfd97a1a0144643ba59350fb84e0dcd21caf50","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2020-07-01T20:48:35Z","title_canon_sha256":"e388d3b51507eb466f79d3430ecf0c442a838b531b85aa512d098d3e38e15f6a"},"schema_version":"1.0","source":{"id":"2007.00748","kind":"arxiv","version":1}},"canonical_sha256":"8039232ff51bca640c46c8c7c4ad7864318d3f407fa9ce0942cfb144e59035d0","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"8039232ff51bca640c46c8c7c4ad7864318d3f407fa9ce0942cfb144e59035d0","first_computed_at":"2026-07-05T01:15:40.847259Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:15:40.847259Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"yqWg8T6hjxHxO3mk7pxWOmfyLnMdr+xHjYaR7LhLjEEJMQ+Z/arDB8Amwmhmm3OQH5IxPoMhcqw/Qs0bX8p+Aw==","signature_status":"signed_v1","signed_at":"2026-07-05T01:15:40.847700Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.00748","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:43f0b1e39c4fe091bf2d55f852b4beb7ca2e10b1c62562987819fec9ee03de01","sha256:827b7faaf1101d190fbbbfedbac6d7efcf7b70cf12952468ffea59b46cf333d5"],"state_sha256":"72998afea79d1b45b869f330ad5d47e7bd2c2fe94cc9b5d04edda54320aa31c5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"2BB6fASDqYb/AdoXmK5U/DnPD48ZbASmmc+yI2GJVJKXRPshYDF9oWRR1NtFYOK2GOyMut1uF4Cjb6wWS0qNAw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-22T04:50:43.837152Z","bundle_sha256":"b9def80d79b0a9856a2a73965a8f93d7e65eab638fd5497521565e4ab4f932dc"}}