{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:ESMIZB3344Y7RBR34DBNNFU7LR","short_pith_number":"pith:ESMIZB33","canonical_record":{"source":{"id":"2304.00990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-03T13:56:01Z","cross_cats_sorted":["cs.LG","cs.PF","stat.ML"],"title_canon_sha256":"e46e4ab52f1e35457cefa0f797ee3a98ec1e6872543308b1be760f70a0406afb","abstract_canon_sha256":"1d23751824fd3f4ffc453760200c141e25659383aa269cd90f3da3233ba5bc15"},"schema_version":"1.0"},"canonical_sha256":"24988c877be731f8863be0c2d6969f5c4780bb5897309ef1adce62038aa772f5","source":{"kind":"arxiv","id":"2304.00990","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.00990","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"arxiv_version","alias_value":"2304.00990v1","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.00990","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"pith_short_12","alias_value":"ESMIZB3344Y7","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"pith_short_16","alias_value":"ESMIZB3344Y7RBR3","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"pith_short_8","alias_value":"ESMIZB33","created_at":"2026-07-05T05:57:25Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:ESMIZB3344Y7RBR34DBNNFU7LR","target":"record","payload":{"canonical_record":{"source":{"id":"2304.00990","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-03T13:56:01Z","cross_cats_sorted":["cs.LG","cs.PF","stat.ML"],"title_canon_sha256":"e46e4ab52f1e35457cefa0f797ee3a98ec1e6872543308b1be760f70a0406afb","abstract_canon_sha256":"1d23751824fd3f4ffc453760200c141e25659383aa269cd90f3da3233ba5bc15"},"schema_version":"1.0"},"canonical_sha256":"24988c877be731f8863be0c2d6969f5c4780bb5897309ef1adce62038aa772f5","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:57:25.298051Z","signature_b64":"UUPAOT6pApVKQsAfPLCOvgWChkff+y79CbUgnZkHzB8nVsV9vowLQmk1RMsrjqhO+ic6JymPLGs3JswTP789BQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"24988c877be731f8863be0c2d6969f5c4780bb5897309ef1adce62038aa772f5","last_reissued_at":"2026-07-05T05:57:25.297616Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:57:25.297616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2304.00990","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":"2026-07-05T05:57:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"NnQfLFivWq8auMcVMpqu++DQ42rJHbTY+Z1NgEQ6NJJP1iDnCTD8UUDGPpE/YdzBJU+5PrFY1xJBPmeV5/sBDA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:06:22.672489Z"},"content_sha256":"f3ae1a4f99eaa89275c7f81f6d4c8d25c6eb5b7f19d230dace8cb3ac5a083fb7","schema_version":"1.0","event_id":"sha256:f3ae1a4f99eaa89275c7f81f6d4c8d25c6eb5b7f19d230dace8cb3ac5a083fb7"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:ESMIZB3344Y7RBR34DBNNFU7LR","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Efficient human-in-loop deep learning model training with iterative refinement and statistical result validation","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","headline":"","cross_cats":["cs.LG","cs.PF","stat.ML"],"primary_cat":"cs.CV","authors_text":"Douglas P. Perrin, Manuel Zahn","submitted_at":"2023-04-03T13:56:01Z","abstract_excerpt":"Annotation and labeling of images are some of the biggest challenges in applying deep learning to medical data. Current processes are time and cost-intensive and, therefore, a limiting factor for the wide adoption of the technology. Additionally validating that measured performance improvements are significant is important to select the best model. In this paper, we demonstrate a method for creating segmentations, a necessary part of a data cleaning for ultrasound imaging machine learning pipelines. We propose a four-step method to leverage automatically generated training data and fast human "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.00990","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/2304.00990/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":"2026-07-05T05:57:25Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"jqhhWbUCBl95V3drgI3ghGd40qfw64L8ezFjQFjSru5RwfbsY5sb0Chx/TLjwbGnG6i3dHXBoq/Agc0PVernCQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T19:06:22.672866Z"},"content_sha256":"a3f06e077c005f7fdda54491fa3cc40758e9ecf376a7ad33e81bcfd16e964725","schema_version":"1.0","event_id":"sha256:a3f06e077c005f7fdda54491fa3cc40758e9ecf376a7ad33e81bcfd16e964725"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/ESMIZB3344Y7RBR34DBNNFU7LR/bundle.json","state_url":"https://pith.science/pith/ESMIZB3344Y7RBR34DBNNFU7LR/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/ESMIZB3344Y7RBR34DBNNFU7LR/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":"2026-07-06T19:06:22Z","links":{"resolver":"https://pith.science/pith/ESMIZB3344Y7RBR34DBNNFU7LR","bundle":"https://pith.science/pith/ESMIZB3344Y7RBR34DBNNFU7LR/bundle.json","state":"https://pith.science/pith/ESMIZB3344Y7RBR34DBNNFU7LR/state.json","well_known_bundle":"https://pith.science/.well-known/pith/ESMIZB3344Y7RBR34DBNNFU7LR/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:ESMIZB3344Y7RBR34DBNNFU7LR","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":"1d23751824fd3f4ffc453760200c141e25659383aa269cd90f3da3233ba5bc15","cross_cats_sorted":["cs.LG","cs.PF","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-03T13:56:01Z","title_canon_sha256":"e46e4ab52f1e35457cefa0f797ee3a98ec1e6872543308b1be760f70a0406afb"},"schema_version":"1.0","source":{"id":"2304.00990","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2304.00990","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"arxiv_version","alias_value":"2304.00990v1","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2304.00990","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"pith_short_12","alias_value":"ESMIZB3344Y7","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"pith_short_16","alias_value":"ESMIZB3344Y7RBR3","created_at":"2026-07-05T05:57:25Z"},{"alias_kind":"pith_short_8","alias_value":"ESMIZB33","created_at":"2026-07-05T05:57:25Z"}],"graph_snapshots":[{"event_id":"sha256:a3f06e077c005f7fdda54491fa3cc40758e9ecf376a7ad33e81bcfd16e964725","target":"graph","created_at":"2026-07-05T05:57:25Z","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/2304.00990/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Annotation and labeling of images are some of the biggest challenges in applying deep learning to medical data. Current processes are time and cost-intensive and, therefore, a limiting factor for the wide adoption of the technology. Additionally validating that measured performance improvements are significant is important to select the best model. In this paper, we demonstrate a method for creating segmentations, a necessary part of a data cleaning for ultrasound imaging machine learning pipelines. We propose a four-step method to leverage automatically generated training data and fast human ","authors_text":"Douglas P. Perrin, Manuel Zahn","cross_cats":["cs.LG","cs.PF","stat.ML"],"headline":"","license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-03T13:56:01Z","title":"Efficient human-in-loop deep learning model training with iterative refinement and statistical result validation"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2304.00990","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:f3ae1a4f99eaa89275c7f81f6d4c8d25c6eb5b7f19d230dace8cb3ac5a083fb7","target":"record","created_at":"2026-07-05T05:57:25Z","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":"1d23751824fd3f4ffc453760200c141e25659383aa269cd90f3da3233ba5bc15","cross_cats_sorted":["cs.LG","cs.PF","stat.ML"],"license":"http://creativecommons.org/licenses/by-nc-nd/4.0/","primary_cat":"cs.CV","submitted_at":"2023-04-03T13:56:01Z","title_canon_sha256":"e46e4ab52f1e35457cefa0f797ee3a98ec1e6872543308b1be760f70a0406afb"},"schema_version":"1.0","source":{"id":"2304.00990","kind":"arxiv","version":1}},"canonical_sha256":"24988c877be731f8863be0c2d6969f5c4780bb5897309ef1adce62038aa772f5","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"24988c877be731f8863be0c2d6969f5c4780bb5897309ef1adce62038aa772f5","first_computed_at":"2026-07-05T05:57:25.297616Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T05:57:25.297616Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"UUPAOT6pApVKQsAfPLCOvgWChkff+y79CbUgnZkHzB8nVsV9vowLQmk1RMsrjqhO+ic6JymPLGs3JswTP789BQ==","signature_status":"signed_v1","signed_at":"2026-07-05T05:57:25.298051Z","signed_message":"canonical_sha256_bytes"},"source_id":"2304.00990","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:f3ae1a4f99eaa89275c7f81f6d4c8d25c6eb5b7f19d230dace8cb3ac5a083fb7","sha256:a3f06e077c005f7fdda54491fa3cc40758e9ecf376a7ad33e81bcfd16e964725"],"state_sha256":"445c045abc2cac1c64dae60f657814d859a67ef5ecd07563d6bba46cdb2fea8e"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XbXLpurIZXmWuB7H+1lwQ2k4mC2ofljpfvsyqkmv1yXe/LbkOC5ns2p3ghjc8IAFY6e237aWEr09GtZw5g5lCg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T19:06:22.674817Z","bundle_sha256":"32879eedfde050791000706338428d4e96b413c9e5daa70dddd516079b5c5c1d"}}