{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2021:VNJE6NSWJWE47VLU7ZM5RVOTDA","short_pith_number":"pith:VNJE6NSW","canonical_record":{"source":{"id":"2105.14314","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-29T14:48:16Z","cross_cats_sorted":[],"title_canon_sha256":"cf8eda35aa0fe31bf25349d51b2c5acb1acb0abadfb9481c6ab4b0b04c6a8047","abstract_canon_sha256":"2e64ebe26eda152a6f9ac3c59a3e93dbb93c1f85f611f7387b8c5cd2ec1dd698"},"schema_version":"1.0"},"canonical_sha256":"ab524f36564d89cfd574fe59d8d5d3183f6daeb5810dd8e34d7343b5270f9e4b","source":{"kind":"arxiv","id":"2105.14314","version":3},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.14314","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"arxiv_version","alias_value":"2105.14314v3","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.14314","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"pith_short_12","alias_value":"VNJE6NSWJWE4","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"pith_short_16","alias_value":"VNJE6NSWJWE47VLU","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"pith_short_8","alias_value":"VNJE6NSW","created_at":"2026-07-05T02:47:45Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2021:VNJE6NSWJWE47VLU7ZM5RVOTDA","target":"record","payload":{"canonical_record":{"source":{"id":"2105.14314","kind":"arxiv","version":3},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-29T14:48:16Z","cross_cats_sorted":[],"title_canon_sha256":"cf8eda35aa0fe31bf25349d51b2c5acb1acb0abadfb9481c6ab4b0b04c6a8047","abstract_canon_sha256":"2e64ebe26eda152a6f9ac3c59a3e93dbb93c1f85f611f7387b8c5cd2ec1dd698"},"schema_version":"1.0"},"canonical_sha256":"ab524f36564d89cfd574fe59d8d5d3183f6daeb5810dd8e34d7343b5270f9e4b","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:47:45.412829Z","signature_b64":"edYnPdRDZ6eFbakbDVCxYLiDyTzuZRMUpzLTCx5kxoksL6jFjq1WQUxlj0taKzVEGOjH2I2QGOpfRTTLoOKgDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ab524f36564d89cfd574fe59d8d5d3183f6daeb5810dd8e34d7343b5270f9e4b","last_reissued_at":"2026-07-05T02:47:45.412252Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:47:45.412252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2105.14314","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":"2026-07-05T02:47:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CA2qJ1RZBBifD/Z02PygbMQbJqP/rfEek6NjDiYLr2J+4m7YiR9j9NRcp4mlqnCpLP3dHTMNMcA1kB7JxWmGBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:59:54.311747Z"},"content_sha256":"5b1b6119c62c99cb33ce0b295a915f863dc3759c74d33da0a641443c3cce3b03","schema_version":"1.0","event_id":"sha256:5b1b6119c62c99cb33ce0b295a915f863dc3759c74d33da0a641443c3cce3b03"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2021:VNJE6NSWJWE47VLU7ZM5RVOTDA","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Dexing Kong, Qinglei Hui, Shaolin Gong, Yuanpeng Liu, Zhiyi Peng","submitted_at":"2021-05-29T14:48:16Z","abstract_excerpt":"Accurate segmentation for medical images is important for clinical diagnosis. Existing automatic segmentation methods are mainly based on fully supervised learning and have an extremely high demand for precise annotations, which are very costly and time-consuming to obtain. To address this problem, we proposed an automatic CT segmentation method based on weakly supervised learning, by which one could train an accurate segmentation model only with weak annotations in the form of bounding boxes. The proposed method is composed of two steps: 1) generating pseudo masks with bounding box annotation"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.14314","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/2105.14314/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":"2026-07-05T02:47:45Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"fkoqQZupgnsQigM3SnrQErp/7asSwEKP8/oDsSNSjVy54GsYgcZIInls9BJBi4r2bluCi1HcRrHwkgcH2omsCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-07T15:59:54.312138Z"},"content_sha256":"4fdefacd0d53cb7427369d7b866fd92abbc8a713d7be7d8c82a028edbc81fb92","schema_version":"1.0","event_id":"sha256:4fdefacd0d53cb7427369d7b866fd92abbc8a713d7be7d8c82a028edbc81fb92"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/VNJE6NSWJWE47VLU7ZM5RVOTDA/bundle.json","state_url":"https://pith.science/pith/VNJE6NSWJWE47VLU7ZM5RVOTDA/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/VNJE6NSWJWE47VLU7ZM5RVOTDA/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":"2026-07-07T15:59:54Z","links":{"resolver":"https://pith.science/pith/VNJE6NSWJWE47VLU7ZM5RVOTDA","bundle":"https://pith.science/pith/VNJE6NSWJWE47VLU7ZM5RVOTDA/bundle.json","state":"https://pith.science/pith/VNJE6NSWJWE47VLU7ZM5RVOTDA/state.json","well_known_bundle":"https://pith.science/.well-known/pith/VNJE6NSWJWE47VLU7ZM5RVOTDA/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2021:VNJE6NSWJWE47VLU7ZM5RVOTDA","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":"2e64ebe26eda152a6f9ac3c59a3e93dbb93c1f85f611f7387b8c5cd2ec1dd698","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-29T14:48:16Z","title_canon_sha256":"cf8eda35aa0fe31bf25349d51b2c5acb1acb0abadfb9481c6ab4b0b04c6a8047"},"schema_version":"1.0","source":{"id":"2105.14314","kind":"arxiv","version":3}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2105.14314","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"arxiv_version","alias_value":"2105.14314v3","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2105.14314","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"pith_short_12","alias_value":"VNJE6NSWJWE4","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"pith_short_16","alias_value":"VNJE6NSWJWE47VLU","created_at":"2026-07-05T02:47:45Z"},{"alias_kind":"pith_short_8","alias_value":"VNJE6NSW","created_at":"2026-07-05T02:47:45Z"}],"graph_snapshots":[{"event_id":"sha256:4fdefacd0d53cb7427369d7b866fd92abbc8a713d7be7d8c82a028edbc81fb92","target":"graph","created_at":"2026-07-05T02:47:45Z","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/2105.14314/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurate segmentation for medical images is important for clinical diagnosis. Existing automatic segmentation methods are mainly based on fully supervised learning and have an extremely high demand for precise annotations, which are very costly and time-consuming to obtain. To address this problem, we proposed an automatic CT segmentation method based on weakly supervised learning, by which one could train an accurate segmentation model only with weak annotations in the form of bounding boxes. The proposed method is composed of two steps: 1) generating pseudo masks with bounding box annotation","authors_text":"Dexing Kong, Qinglei Hui, Shaolin Gong, Yuanpeng Liu, Zhiyi Peng","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-29T14:48:16Z","title":"Automatic CT Segmentation from Bounding Box Annotations using Convolutional Neural Networks"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2105.14314","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":"sha256:5b1b6119c62c99cb33ce0b295a915f863dc3759c74d33da0a641443c3cce3b03","target":"record","created_at":"2026-07-05T02:47:45Z","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":"2e64ebe26eda152a6f9ac3c59a3e93dbb93c1f85f611f7387b8c5cd2ec1dd698","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2021-05-29T14:48:16Z","title_canon_sha256":"cf8eda35aa0fe31bf25349d51b2c5acb1acb0abadfb9481c6ab4b0b04c6a8047"},"schema_version":"1.0","source":{"id":"2105.14314","kind":"arxiv","version":3}},"canonical_sha256":"ab524f36564d89cfd574fe59d8d5d3183f6daeb5810dd8e34d7343b5270f9e4b","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"ab524f36564d89cfd574fe59d8d5d3183f6daeb5810dd8e34d7343b5270f9e4b","first_computed_at":"2026-07-05T02:47:45.412252Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T02:47:45.412252Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"edYnPdRDZ6eFbakbDVCxYLiDyTzuZRMUpzLTCx5kxoksL6jFjq1WQUxlj0taKzVEGOjH2I2QGOpfRTTLoOKgDA==","signature_status":"signed_v1","signed_at":"2026-07-05T02:47:45.412829Z","signed_message":"canonical_sha256_bytes"},"source_id":"2105.14314","source_kind":"arxiv","source_version":3}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:5b1b6119c62c99cb33ce0b295a915f863dc3759c74d33da0a641443c3cce3b03","sha256:4fdefacd0d53cb7427369d7b866fd92abbc8a713d7be7d8c82a028edbc81fb92"],"state_sha256":"4192b56ca85b2e1f08f20652053bb59ae3648576bf8757888f2eed2fefa06559"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Og0AordS0nzHZZfs3gJp06ILinS5IAz3QJFYQ/ynoDt24qi9ErieIQRv4XQ4hVj9T4RX+h2Kzz4hsHO+uTLTDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-07T15:59:54.314225Z","bundle_sha256":"b95f9f9aa7af2cb33f1dd41e1bd9aec0f8999aa4fe1adb471c2db828b312ed4a"}}