{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2020:SFLGCPI3CXL7GTZ3ZCO267P4DP","short_pith_number":"pith:SFLGCPI3","canonical_record":{"source":{"id":"2007.07012","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-07-07T16:38:04Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"589bcbaf0d9b8b1db356d0cdf81b795ff4c4d702abb4329f70222cbd7e2e88e1","abstract_canon_sha256":"351c834abc25d5525df1be8fe1de198c87e6b205155b374a6ac49451386e3e9a"},"schema_version":"1.0"},"canonical_sha256":"9156613d1b15d7f34f3bc89daf7dfc1bfe6dfe8cda020f6193826f5fbcb18a4f","source":{"kind":"arxiv","id":"2007.07012","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.07012","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"arxiv_version","alias_value":"2007.07012v1","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.07012","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"pith_short_12","alias_value":"SFLGCPI3CXL7","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"pith_short_16","alias_value":"SFLGCPI3CXL7GTZ3","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"pith_short_8","alias_value":"SFLGCPI3","created_at":"2026-07-05T01:18:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2020:SFLGCPI3CXL7GTZ3ZCO267P4DP","target":"record","payload":{"canonical_record":{"source":{"id":"2007.07012","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-07-07T16:38:04Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"589bcbaf0d9b8b1db356d0cdf81b795ff4c4d702abb4329f70222cbd7e2e88e1","abstract_canon_sha256":"351c834abc25d5525df1be8fe1de198c87e6b205155b374a6ac49451386e3e9a"},"schema_version":"1.0"},"canonical_sha256":"9156613d1b15d7f34f3bc89daf7dfc1bfe6dfe8cda020f6193826f5fbcb18a4f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T01:18:25.866135Z","signature_b64":"zmbvMpmjkjFAgW4d4VwRnsZfUDaG/YafofWKaOak6jdsI+cDo4Wj7dj6lL//mMRB41xxwGvsqu5pgmH8CMX4DQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"9156613d1b15d7f34f3bc89daf7dfc1bfe6dfe8cda020f6193826f5fbcb18a4f","last_reissued_at":"2026-07-05T01:18:25.865711Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T01:18:25.865711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2007.07012","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":"2026-07-05T01:18:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3AK+G5dEbGy91i6VqNm+9aJrkiV7tPlKsTMCY74Yf2ZPv/R0+zVEnokeRF0yRuLRvHcDg7fQNQLSKeirbQN4CQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:10:23.580684Z"},"content_sha256":"7f5390563edd7d40077f6c305483f2a7342058bcdca977edd999bfdfcd981519","schema_version":"1.0","event_id":"sha256:7f5390563edd7d40077f6c305483f2a7342058bcdca977edd999bfdfcd981519"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2020:SFLGCPI3CXL7GTZ3ZCO267P4DP","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"David Vazquez, Derek Nowrouzezahrai, Frederic Branchaud-Charron, Issam Laradji, Keegan Lensink, Parmida Atighehchian, Pau Rodriguez, William Parker","submitted_at":"2020-07-07T16:38:04Z","abstract_excerpt":"One of the key challenges in the battle against the Coronavirus (COVID-19) pandemic is to detect and quantify the severity of the disease in a timely manner. Computed tomographies (CT) of the lungs are effective for assessing the state of the infection. Unfortunately, labeling CT scans can take a lot of time and effort, with up to 150 minutes per scan. We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images. Conventionally, active learning methods require the labelers to annotate whole images with full supervis"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.07012","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/2007.07012/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":"2026-07-05T01:18:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"gjfpz/5oMzW8qnhWe8MCU17ne594IZ1SROYzL5S+Zkq4JgtFiQquX2bu3mpOMoWGUMwL7RHMWhJqF8hZQ5R3AA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T11:10:23.581053Z"},"content_sha256":"c6223fee32c5f4c13528a3597f3879e3dca364d6d2aed704717abb85716240b3","schema_version":"1.0","event_id":"sha256:c6223fee32c5f4c13528a3597f3879e3dca364d6d2aed704717abb85716240b3"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SFLGCPI3CXL7GTZ3ZCO267P4DP/bundle.json","state_url":"https://pith.science/pith/SFLGCPI3CXL7GTZ3ZCO267P4DP/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SFLGCPI3CXL7GTZ3ZCO267P4DP/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":"2026-07-07T11:10:23Z","links":{"resolver":"https://pith.science/pith/SFLGCPI3CXL7GTZ3ZCO267P4DP","bundle":"https://pith.science/pith/SFLGCPI3CXL7GTZ3ZCO267P4DP/bundle.json","state":"https://pith.science/pith/SFLGCPI3CXL7GTZ3ZCO267P4DP/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SFLGCPI3CXL7GTZ3ZCO267P4DP/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2020:SFLGCPI3CXL7GTZ3ZCO267P4DP","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":"351c834abc25d5525df1be8fe1de198c87e6b205155b374a6ac49451386e3e9a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-07-07T16:38:04Z","title_canon_sha256":"589bcbaf0d9b8b1db356d0cdf81b795ff4c4d702abb4329f70222cbd7e2e88e1"},"schema_version":"1.0","source":{"id":"2007.07012","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2007.07012","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"arxiv_version","alias_value":"2007.07012v1","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2007.07012","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"pith_short_12","alias_value":"SFLGCPI3CXL7","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"pith_short_16","alias_value":"SFLGCPI3CXL7GTZ3","created_at":"2026-07-05T01:18:25Z"},{"alias_kind":"pith_short_8","alias_value":"SFLGCPI3","created_at":"2026-07-05T01:18:25Z"}],"graph_snapshots":[{"event_id":"sha256:c6223fee32c5f4c13528a3597f3879e3dca364d6d2aed704717abb85716240b3","target":"graph","created_at":"2026-07-05T01:18:25Z","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/2007.07012/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"One of the key challenges in the battle against the Coronavirus (COVID-19) pandemic is to detect and quantify the severity of the disease in a timely manner. Computed tomographies (CT) of the lungs are effective for assessing the state of the infection. Unfortunately, labeling CT scans can take a lot of time and effort, with up to 150 minutes per scan. We address this challenge introducing a scalable, fast, and accurate active learning system that accelerates the labeling of CT scan images. Conventionally, active learning methods require the labelers to annotate whole images with full supervis","authors_text":"David Vazquez, Derek Nowrouzezahrai, Frederic Branchaud-Charron, Issam Laradji, Keegan Lensink, Parmida Atighehchian, Pau Rodriguez, William Parker","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-07-07T16:38:04Z","title":"A Weakly Supervised Region-Based Active Learning Method for COVID-19 Segmentation in CT Images"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2007.07012","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":"sha256:7f5390563edd7d40077f6c305483f2a7342058bcdca977edd999bfdfcd981519","target":"record","created_at":"2026-07-05T01:18:25Z","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":"351c834abc25d5525df1be8fe1de198c87e6b205155b374a6ac49451386e3e9a","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2020-07-07T16:38:04Z","title_canon_sha256":"589bcbaf0d9b8b1db356d0cdf81b795ff4c4d702abb4329f70222cbd7e2e88e1"},"schema_version":"1.0","source":{"id":"2007.07012","kind":"arxiv","version":1}},"canonical_sha256":"9156613d1b15d7f34f3bc89daf7dfc1bfe6dfe8cda020f6193826f5fbcb18a4f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"9156613d1b15d7f34f3bc89daf7dfc1bfe6dfe8cda020f6193826f5fbcb18a4f","first_computed_at":"2026-07-05T01:18:25.865711Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T01:18:25.865711Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"zmbvMpmjkjFAgW4d4VwRnsZfUDaG/YafofWKaOak6jdsI+cDo4Wj7dj6lL//mMRB41xxwGvsqu5pgmH8CMX4DQ==","signature_status":"signed_v1","signed_at":"2026-07-05T01:18:25.866135Z","signed_message":"canonical_sha256_bytes"},"source_id":"2007.07012","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7f5390563edd7d40077f6c305483f2a7342058bcdca977edd999bfdfcd981519","sha256:c6223fee32c5f4c13528a3597f3879e3dca364d6d2aed704717abb85716240b3"],"state_sha256":"e7348efa122aa60b374c417b308ea239770f6641668a8f54673bc9af44eb9a4d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"iZ8umcvWK1Gfq90lpQ7iP6zF6IaFpBdCjWSMyIHu71rmp1E14bRSSAr3u5rESpfBtggUm+0o7RYfvCgorFXoAg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T11:10:23.583630Z","bundle_sha256":"91ff14fc70c140d18376ad5bcdd09e2d658919bc586caf868aa6e9bad4359bed"}}