{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:IZTQQ52TXTMAE74FN4Z5ZCGNKH","short_pith_number":"pith:IZTQQ52T","canonical_record":{"source":{"id":"1906.02421","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T05:19:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c2671f5cd012ed7ade80df40e4d3e5c9c2b4a5d43a19cf760eea615fc8f4eb0b","abstract_canon_sha256":"5cab8732850efd22e2df56b378199fca74b0a8a873b9ef0182f975a418034ce4"},"schema_version":"1.0"},"canonical_sha256":"4667087753bcd8027f856f33dc88cd51f7c91ca9768cd19ca27907852b59cbc2","source":{"kind":"arxiv","id":"1906.02421","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.02421","created_at":"2026-05-17T23:44:01Z"},{"alias_kind":"arxiv_version","alias_value":"1906.02421v1","created_at":"2026-05-17T23:44:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.02421","created_at":"2026-05-17T23:44:01Z"},{"alias_kind":"pith_short_12","alias_value":"IZTQQ52TXTMA","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"IZTQQ52TXTMAE74F","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"IZTQQ52T","created_at":"2026-05-18T12:33:18Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:IZTQQ52TXTMAE74FN4Z5ZCGNKH","target":"record","payload":{"canonical_record":{"source":{"id":"1906.02421","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T05:19:22Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"c2671f5cd012ed7ade80df40e4d3e5c9c2b4a5d43a19cf760eea615fc8f4eb0b","abstract_canon_sha256":"5cab8732850efd22e2df56b378199fca74b0a8a873b9ef0182f975a418034ce4"},"schema_version":"1.0"},"canonical_sha256":"4667087753bcd8027f856f33dc88cd51f7c91ca9768cd19ca27907852b59cbc2","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-17T23:44:01.267833Z","signature_b64":"Z1hw/5C8hgILZfnWFvW8+5zq274CMBPj2NV5RGlqb9E3ZLj7xfl86hAMQVZ6YhUkSouvUaL8uInlGlt8959XDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4667087753bcd8027f856f33dc88cd51f7c91ca9768cd19ca27907852b59cbc2","last_reissued_at":"2026-05-17T23:44:01.267296Z","signature_status":"signed_v1","first_computed_at":"2026-05-17T23:44:01.267296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1906.02421","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":"2026-05-17T23:44:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"aZkDuNTo5l/Wi61tZJLBtP8zd/l6f+QYTqVma88SeSUUWQyUVJqgHiiicPfoZxC8Rb40c82xsUtmUdKSef1qBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T08:56:12.214864Z"},"content_sha256":"6474a754ea3e01e31c97bfbab9c5e90fb55f2eba4d33f5b55e5fa55e522e17a4","schema_version":"1.0","event_id":"sha256:6474a754ea3e01e31c97bfbab9c5e90fb55f2eba4d33f5b55e5fa55e522e17a4"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:IZTQQ52TXTMAE74FN4Z5ZCGNKH","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"cs.CV","authors_text":"Ahmed H. Shahin, Javier Villafruela, Jianbing Shen, Ling Shao, Shadab Khan","submitted_at":"2019-06-06T05:19:22Z","abstract_excerpt":"To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model that tries to emulate the expert labeling on a new data set. While tremendous progress has been made using such approaches, labeling of medical images remains a time-consuming and expensive task. In this paper, we evaluate the utility of extreme points in learning to segment. Specifically, we propose a novel approach to compute a confidence map from extreme points that quantitatively encodes the priors derived from extr"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.02421","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":"2026-05-17T23:44:01Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"sQnjpzcjSbnDcckaFNknJTzgVSRuFyEIcxxYfhwVex8KiYCe2nC34iJ19qg3otSwo2QoKtpg8FUKb/3nnImgAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-02T08:56:12.215204Z"},"content_sha256":"6bf3dc0cfd7d22ab2ff92b7574be4381e51927ef63e7e345070a18c93eed5081","schema_version":"1.0","event_id":"sha256:6bf3dc0cfd7d22ab2ff92b7574be4381e51927ef63e7e345070a18c93eed5081"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/IZTQQ52TXTMAE74FN4Z5ZCGNKH/bundle.json","state_url":"https://pith.science/pith/IZTQQ52TXTMAE74FN4Z5ZCGNKH/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/IZTQQ52TXTMAE74FN4Z5ZCGNKH/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":"2026-07-02T08:56:12Z","links":{"resolver":"https://pith.science/pith/IZTQQ52TXTMAE74FN4Z5ZCGNKH","bundle":"https://pith.science/pith/IZTQQ52TXTMAE74FN4Z5ZCGNKH/bundle.json","state":"https://pith.science/pith/IZTQQ52TXTMAE74FN4Z5ZCGNKH/state.json","well_known_bundle":"https://pith.science/.well-known/pith/IZTQQ52TXTMAE74FN4Z5ZCGNKH/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:IZTQQ52TXTMAE74FN4Z5ZCGNKH","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":"5cab8732850efd22e2df56b378199fca74b0a8a873b9ef0182f975a418034ce4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T05:19:22Z","title_canon_sha256":"c2671f5cd012ed7ade80df40e4d3e5c9c2b4a5d43a19cf760eea615fc8f4eb0b"},"schema_version":"1.0","source":{"id":"1906.02421","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1906.02421","created_at":"2026-05-17T23:44:01Z"},{"alias_kind":"arxiv_version","alias_value":"1906.02421v1","created_at":"2026-05-17T23:44:01Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1906.02421","created_at":"2026-05-17T23:44:01Z"},{"alias_kind":"pith_short_12","alias_value":"IZTQQ52TXTMA","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_16","alias_value":"IZTQQ52TXTMAE74F","created_at":"2026-05-18T12:33:18Z"},{"alias_kind":"pith_short_8","alias_value":"IZTQQ52T","created_at":"2026-05-18T12:33:18Z"}],"graph_snapshots":[{"event_id":"sha256:6bf3dc0cfd7d22ab2ff92b7574be4381e51927ef63e7e345070a18c93eed5081","target":"graph","created_at":"2026-05-17T23:44:01Z","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":"To automate the process of segmenting an anatomy of interest, we can learn a model from previously annotated data. The learning-based approach uses annotations to train a model that tries to emulate the expert labeling on a new data set. While tremendous progress has been made using such approaches, labeling of medical images remains a time-consuming and expensive task. In this paper, we evaluate the utility of extreme points in learning to segment. Specifically, we propose a novel approach to compute a confidence map from extreme points that quantitatively encodes the priors derived from extr","authors_text":"Ahmed H. Shahin, Javier Villafruela, Jianbing Shen, Ling Shao, Shadab Khan","cross_cats":["cs.LG"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T05:19:22Z","title":"Extreme Points Derived Confidence Map as a Cue For Class-Agnostic Segmentation Using Deep Neural Network"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1906.02421","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":"sha256:6474a754ea3e01e31c97bfbab9c5e90fb55f2eba4d33f5b55e5fa55e522e17a4","target":"record","created_at":"2026-05-17T23:44:01Z","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":"5cab8732850efd22e2df56b378199fca74b0a8a873b9ef0182f975a418034ce4","cross_cats_sorted":["cs.LG"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2019-06-06T05:19:22Z","title_canon_sha256":"c2671f5cd012ed7ade80df40e4d3e5c9c2b4a5d43a19cf760eea615fc8f4eb0b"},"schema_version":"1.0","source":{"id":"1906.02421","kind":"arxiv","version":1}},"canonical_sha256":"4667087753bcd8027f856f33dc88cd51f7c91ca9768cd19ca27907852b59cbc2","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"4667087753bcd8027f856f33dc88cd51f7c91ca9768cd19ca27907852b59cbc2","first_computed_at":"2026-05-17T23:44:01.267296Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-17T23:44:01.267296Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Z1hw/5C8hgILZfnWFvW8+5zq274CMBPj2NV5RGlqb9E3ZLj7xfl86hAMQVZ6YhUkSouvUaL8uInlGlt8959XDw==","signature_status":"signed_v1","signed_at":"2026-05-17T23:44:01.267833Z","signed_message":"canonical_sha256_bytes"},"source_id":"1906.02421","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:6474a754ea3e01e31c97bfbab9c5e90fb55f2eba4d33f5b55e5fa55e522e17a4","sha256:6bf3dc0cfd7d22ab2ff92b7574be4381e51927ef63e7e345070a18c93eed5081"],"state_sha256":"a94fa80e524719af002fed75cb373175fcbe00ade581a34cc1c6931e599a8512"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"EQuNrqP3SC1ulQZvyMmmW3ucjvk1RCmIxm3gnl8+fnp/5NubDJEOTvNjwO1ZOa6xzK7WMLvUT76J4sQt7sBODw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-02T08:56:12.217107Z","bundle_sha256":"c5e82f09b0ee93392342a7976e197ab9b04f9cbc51c0fb6de9a26771c9ffb942"}}