{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2023:P3HIZ2ML2ILXOSJWWRA74AMCEO","short_pith_number":"pith:P3HIZ2ML","canonical_record":{"source":{"id":"2310.01529","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-02T18:17:20Z","cross_cats_sorted":[],"title_canon_sha256":"816c56c9e352ea5ac0743a6b48d93830430bdce4d029a55214ab01f6a1f0da26","abstract_canon_sha256":"3c4cfe0aae3ab14cfa7d2973fa9911c1f3b920a79e6f2386e79b8ee66761cd87"},"schema_version":"1.0"},"canonical_sha256":"7ece8ce98bd217774936b441fe018223830cb835815ed9f95cf68d6f0c9daf03","source":{"kind":"arxiv","id":"2310.01529","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.01529","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"arxiv_version","alias_value":"2310.01529v1","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.01529","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"pith_short_12","alias_value":"P3HIZ2ML2ILX","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"pith_short_16","alias_value":"P3HIZ2ML2ILXOSJW","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"pith_short_8","alias_value":"P3HIZ2ML","created_at":"2026-07-05T06:56:41Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2023:P3HIZ2ML2ILXOSJWWRA74AMCEO","target":"record","payload":{"canonical_record":{"source":{"id":"2310.01529","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-02T18:17:20Z","cross_cats_sorted":[],"title_canon_sha256":"816c56c9e352ea5ac0743a6b48d93830430bdce4d029a55214ab01f6a1f0da26","abstract_canon_sha256":"3c4cfe0aae3ab14cfa7d2973fa9911c1f3b920a79e6f2386e79b8ee66761cd87"},"schema_version":"1.0"},"canonical_sha256":"7ece8ce98bd217774936b441fe018223830cb835815ed9f95cf68d6f0c9daf03","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:56:41.456201Z","signature_b64":"Fk1JDYkqvplbXu0EP3rWtlusTY7Kfdl2Chu9a67noGb68QlhPR1YLwxrK3V4c/KstbxVU0oGOGBrcZRWPGKqBQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"7ece8ce98bd217774936b441fe018223830cb835815ed9f95cf68d6f0c9daf03","last_reissued_at":"2026-07-05T06:56:41.455729Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:56:41.455729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2310.01529","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":"2026-07-05T06:56:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"hL+cOuWz+CX95scobcLuW9FT4I1SeYW4+Yg9HO4xJsPJVPj+GDm/B3IiM7NSYt0Kn3d7GPaz5MooiJh2IG2HAA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T16:20:01.105341Z"},"content_sha256":"7a4a4b5595b3aafe30132852e3fb63c34fb87f1a990797e6d5b20d47a2ae9007","schema_version":"1.0","event_id":"sha256:7a4a4b5595b3aafe30132852e3fb63c34fb87f1a990797e6d5b20d47a2ae9007"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2023:P3HIZ2ML2ILXOSJWWRA74AMCEO","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Abu Zahid Bin Aziz, Jadie Adams, Shireen Elhabian","submitted_at":"2023-10-02T18:17:20Z","abstract_excerpt":"Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these applications still has a long way to go. Recent deep learning methods have paved the way for reducing the substantial preprocessing steps to construct SSMs directly from unsegmented images. Nevertheless, the performance of these models is not up to the mark. Inspired by multiscale/multiresolution learning, we propose a new training strategy, progressive DeepSSM, to train image-to-shape deep learning models. The training is perf"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.01529","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/2310.01529/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":"2026-07-05T06:56:41Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"CuekWYrVvaOKzyIMdpaaqeBL046r94qxVKh7WDYOQL0YMuM2ys2t/YXNUxne0f4e+ONwZUEECL3Vfh4o3+qyDg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-07-08T16:20:01.105722Z"},"content_sha256":"edb29b746206bd6588863540a206fe42f4531a2e81a3f2bfae05c1ae880a38a4","schema_version":"1.0","event_id":"sha256:edb29b746206bd6588863540a206fe42f4531a2e81a3f2bfae05c1ae880a38a4"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/P3HIZ2ML2ILXOSJWWRA74AMCEO/bundle.json","state_url":"https://pith.science/pith/P3HIZ2ML2ILXOSJWWRA74AMCEO/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/P3HIZ2ML2ILXOSJWWRA74AMCEO/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":"2026-07-08T16:20:01Z","links":{"resolver":"https://pith.science/pith/P3HIZ2ML2ILXOSJWWRA74AMCEO","bundle":"https://pith.science/pith/P3HIZ2ML2ILXOSJWWRA74AMCEO/bundle.json","state":"https://pith.science/pith/P3HIZ2ML2ILXOSJWWRA74AMCEO/state.json","well_known_bundle":"https://pith.science/.well-known/pith/P3HIZ2ML2ILXOSJWWRA74AMCEO/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2023:P3HIZ2ML2ILXOSJWWRA74AMCEO","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":"3c4cfe0aae3ab14cfa7d2973fa9911c1f3b920a79e6f2386e79b8ee66761cd87","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-02T18:17:20Z","title_canon_sha256":"816c56c9e352ea5ac0743a6b48d93830430bdce4d029a55214ab01f6a1f0da26"},"schema_version":"1.0","source":{"id":"2310.01529","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2310.01529","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"arxiv_version","alias_value":"2310.01529v1","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.01529","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"pith_short_12","alias_value":"P3HIZ2ML2ILX","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"pith_short_16","alias_value":"P3HIZ2ML2ILXOSJW","created_at":"2026-07-05T06:56:41Z"},{"alias_kind":"pith_short_8","alias_value":"P3HIZ2ML","created_at":"2026-07-05T06:56:41Z"}],"graph_snapshots":[{"event_id":"sha256:edb29b746206bd6588863540a206fe42f4531a2e81a3f2bfae05c1ae880a38a4","target":"graph","created_at":"2026-07-05T06:56:41Z","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/2310.01529/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Statistical shape modeling (SSM) is an enabling quantitative tool to study anatomical shapes in various medical applications. However, directly using 3D images in these applications still has a long way to go. Recent deep learning methods have paved the way for reducing the substantial preprocessing steps to construct SSMs directly from unsegmented images. Nevertheless, the performance of these models is not up to the mark. Inspired by multiscale/multiresolution learning, we propose a new training strategy, progressive DeepSSM, to train image-to-shape deep learning models. The training is perf","authors_text":"Abu Zahid Bin Aziz, Jadie Adams, Shireen Elhabian","cross_cats":[],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-02T18:17:20Z","title":"Progressive DeepSSM: Training Methodology for Image-To-Shape Deep Models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.01529","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":"sha256:7a4a4b5595b3aafe30132852e3fb63c34fb87f1a990797e6d5b20d47a2ae9007","target":"record","created_at":"2026-07-05T06:56:41Z","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":"3c4cfe0aae3ab14cfa7d2973fa9911c1f3b920a79e6f2386e79b8ee66761cd87","cross_cats_sorted":[],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-02T18:17:20Z","title_canon_sha256":"816c56c9e352ea5ac0743a6b48d93830430bdce4d029a55214ab01f6a1f0da26"},"schema_version":"1.0","source":{"id":"2310.01529","kind":"arxiv","version":1}},"canonical_sha256":"7ece8ce98bd217774936b441fe018223830cb835815ed9f95cf68d6f0c9daf03","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"7ece8ce98bd217774936b441fe018223830cb835815ed9f95cf68d6f0c9daf03","first_computed_at":"2026-07-05T06:56:41.455729Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T06:56:41.455729Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"Fk1JDYkqvplbXu0EP3rWtlusTY7Kfdl2Chu9a67noGb68QlhPR1YLwxrK3V4c/KstbxVU0oGOGBrcZRWPGKqBQ==","signature_status":"signed_v1","signed_at":"2026-07-05T06:56:41.456201Z","signed_message":"canonical_sha256_bytes"},"source_id":"2310.01529","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:7a4a4b5595b3aafe30132852e3fb63c34fb87f1a990797e6d5b20d47a2ae9007","sha256:edb29b746206bd6588863540a206fe42f4531a2e81a3f2bfae05c1ae880a38a4"],"state_sha256":"71089c4df1bf09afa69d091cde3b34f1a333955e0314e75c7b79e47c780e962d"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"XxEVssgqBjZGOLxk0CT19d3bw/fOcEyACRu/beJcpQgPus1lkT3gTVXVEhaEpejxyWCBkABTRYD0dnDa+F21Cg==","signed_message":"bundle_sha256_bytes","signed_at":"2026-07-08T16:20:01.107954Z","bundle_sha256":"36e69cd98e75881111a4200eb9da7cc8777fa1b27b5f2ce4d034a29cab24dae6"}}