{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2019:3MUETIR5TKIX5A3SLQILRB6SSQ","short_pith_number":"pith:3MUETIR5","canonical_record":{"source":{"id":"1908.06912","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-08-19T16:20:39Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"36432683dbc7ec1a41a19cba593fe2ea5193b3b1d1361c3fb51808c1c5e60976","abstract_canon_sha256":"4fdcc650f70bf9cf2399fa6bc3765368ac798a3cdefbc3ae9df75757a29a647d"},"schema_version":"1.0"},"canonical_sha256":"db2849a23d9a917e83725c10b887d2940eb6bdd9854bb248ff804dd4d3520516","source":{"kind":"arxiv","id":"1908.06912","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06912","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06912v1","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06912","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"pith_short_12","alias_value":"3MUETIR5TKIX","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"pith_short_16","alias_value":"3MUETIR5TKIX5A3S","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"pith_short_8","alias_value":"3MUETIR5","created_at":"2026-07-04T23:58:20Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2019:3MUETIR5TKIX5A3SLQILRB6SSQ","target":"record","payload":{"canonical_record":{"source":{"id":"1908.06912","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-08-19T16:20:39Z","cross_cats_sorted":["cs.CV"],"title_canon_sha256":"36432683dbc7ec1a41a19cba593fe2ea5193b3b1d1361c3fb51808c1c5e60976","abstract_canon_sha256":"4fdcc650f70bf9cf2399fa6bc3765368ac798a3cdefbc3ae9df75757a29a647d"},"schema_version":"1.0"},"canonical_sha256":"db2849a23d9a917e83725c10b887d2940eb6bdd9854bb248ff804dd4d3520516","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-04T23:58:20.890813Z","signature_b64":"HC0DNYZOl9JnRC6FsUrc9iXv5tl6lQfPcJ3G40ux8/TTXW0vM5VeR+uKuiFS58vMcQBNEb3MIh13Y0Lo5y49AA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"db2849a23d9a917e83725c10b887d2940eb6bdd9854bb248ff804dd4d3520516","last_reissued_at":"2026-07-04T23:58:20.890387Z","signature_status":"signed_v1","first_computed_at":"2026-07-04T23:58:20.890387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"1908.06912","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":"2026-07-04T23:58:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"LQ82iD5atdD2FBqX+Nvpy2SGUmQFOgYNCfJ0T1DVBZS5TB7YY45qn5Eq88l9zTjPmQvCPK/NKbNoA5UfoCD0Bw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:27:48.447303Z"},"content_sha256":"0086515589cdc7e1e77614797f11dfd51d29522ee56a0a54822a162c67cb5305","schema_version":"1.0","event_id":"sha256:0086515589cdc7e1e77614797f11dfd51d29522ee56a0a54822a162c67cb5305"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2019:3MUETIR5TKIX5A3SLQILRB6SSQ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV"],"primary_cat":"eess.IV","authors_text":"Jianming Liang, Md Mahfuzur Rahman Siddiquee, Michael B. Gotway, Nima Tajbakhsh, Ruibin Feng, Vatsal Sodha, Zongwei Zhou","submitted_at":"2019-08-19T16:20:39Z","abstract_excerpt":"Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information and inevitably compromising the performance. To overcome this limitation, we have built a set of models, called Generic Autodidactic Models, nicknamed Models Genesis, because they are created ex nihilo (with no manual labeling), self-taught (learned by self-"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06912","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/1908.06912/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":"2026-07-04T23:58:20Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"RPtnrS8aVAy0UiOYMATtDnoHpUWANQu9XxDIMQ95x/sdeQd+t5ix9AgnvPxw/2vllIwbjkD73+yISXEdtUdhCg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-06T20:27:48.447701Z"},"content_sha256":"266049a404db5ea9dc57bcd6945e915d8b45ce59aa38f355949761d3084e8978","schema_version":"1.0","event_id":"sha256:266049a404db5ea9dc57bcd6945e915d8b45ce59aa38f355949761d3084e8978"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/3MUETIR5TKIX5A3SLQILRB6SSQ/bundle.json","state_url":"https://pith.science/pith/3MUETIR5TKIX5A3SLQILRB6SSQ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/3MUETIR5TKIX5A3SLQILRB6SSQ/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":"2026-07-06T20:27:48Z","links":{"resolver":"https://pith.science/pith/3MUETIR5TKIX5A3SLQILRB6SSQ","bundle":"https://pith.science/pith/3MUETIR5TKIX5A3SLQILRB6SSQ/bundle.json","state":"https://pith.science/pith/3MUETIR5TKIX5A3SLQILRB6SSQ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/3MUETIR5TKIX5A3SLQILRB6SSQ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2019:3MUETIR5TKIX5A3SLQILRB6SSQ","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":"4fdcc650f70bf9cf2399fa6bc3765368ac798a3cdefbc3ae9df75757a29a647d","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-08-19T16:20:39Z","title_canon_sha256":"36432683dbc7ec1a41a19cba593fe2ea5193b3b1d1361c3fb51808c1c5e60976"},"schema_version":"1.0","source":{"id":"1908.06912","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"1908.06912","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"arxiv_version","alias_value":"1908.06912v1","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1908.06912","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"pith_short_12","alias_value":"3MUETIR5TKIX","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"pith_short_16","alias_value":"3MUETIR5TKIX5A3S","created_at":"2026-07-04T23:58:20Z"},{"alias_kind":"pith_short_8","alias_value":"3MUETIR5","created_at":"2026-07-04T23:58:20Z"}],"graph_snapshots":[{"event_id":"sha256:266049a404db5ea9dc57bcd6945e915d8b45ce59aa38f355949761d3084e8978","target":"graph","created_at":"2026-07-04T23:58:20Z","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/1908.06912/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Transfer learning from natural image to medical image has established as one of the most practical paradigms in deep learning for medical image analysis. However, to fit this paradigm, 3D imaging tasks in the most prominent imaging modalities (e.g., CT and MRI) have to be reformulated and solved in 2D, losing rich 3D anatomical information and inevitably compromising the performance. To overcome this limitation, we have built a set of models, called Generic Autodidactic Models, nicknamed Models Genesis, because they are created ex nihilo (with no manual labeling), self-taught (learned by self-","authors_text":"Jianming Liang, Md Mahfuzur Rahman Siddiquee, Michael B. Gotway, Nima Tajbakhsh, Ruibin Feng, Vatsal Sodha, Zongwei Zhou","cross_cats":["cs.CV"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-08-19T16:20:39Z","title":"Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1908.06912","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":"sha256:0086515589cdc7e1e77614797f11dfd51d29522ee56a0a54822a162c67cb5305","target":"record","created_at":"2026-07-04T23:58:20Z","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":"4fdcc650f70bf9cf2399fa6bc3765368ac798a3cdefbc3ae9df75757a29a647d","cross_cats_sorted":["cs.CV"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"eess.IV","submitted_at":"2019-08-19T16:20:39Z","title_canon_sha256":"36432683dbc7ec1a41a19cba593fe2ea5193b3b1d1361c3fb51808c1c5e60976"},"schema_version":"1.0","source":{"id":"1908.06912","kind":"arxiv","version":1}},"canonical_sha256":"db2849a23d9a917e83725c10b887d2940eb6bdd9854bb248ff804dd4d3520516","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"db2849a23d9a917e83725c10b887d2940eb6bdd9854bb248ff804dd4d3520516","first_computed_at":"2026-07-04T23:58:20.890387Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-04T23:58:20.890387Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HC0DNYZOl9JnRC6FsUrc9iXv5tl6lQfPcJ3G40ux8/TTXW0vM5VeR+uKuiFS58vMcQBNEb3MIh13Y0Lo5y49AA==","signature_status":"signed_v1","signed_at":"2026-07-04T23:58:20.890813Z","signed_message":"canonical_sha256_bytes"},"source_id":"1908.06912","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:0086515589cdc7e1e77614797f11dfd51d29522ee56a0a54822a162c67cb5305","sha256:266049a404db5ea9dc57bcd6945e915d8b45ce59aa38f355949761d3084e8978"],"state_sha256":"13347a6b7d1af6f2f3e1239ae41b83713bf4a09b177a4bcf32122a696eb801f2"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"m2IsWZfIelJH4B3M0o8bXqVBS8jyclb0udqc1hn9xCawMATKfWfPqWjEOLJjyP7Yf1M/XAfZ/p/wD7JO1j1FDg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-06T20:27:48.449733Z","bundle_sha256":"de85edbfbcf4fedd36b7aa915a19d975f8d973fb584ee7d96f6daf3924d6c068"}}