{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2025:SJQBX2F7B24N6WP4PWYZ3O4CPG","short_pith_number":"pith:SJQBX2F7","canonical_record":{"source":{"id":"2506.23688","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2025-06-30T10:08:25Z","cross_cats_sorted":[],"title_canon_sha256":"3252bbb8776a568939a4667eef9af3a75a10fc6b2bdd8d8fda2becb446c11fee","abstract_canon_sha256":"5d392fefb2e5e7cfa3543aaf52fe3a2809e0d78d526d90f7d2561e55d956f1b9"},"schema_version":"1.0"},"canonical_sha256":"92601be8bf0eb8df59fc7db19dbb827998b9f6bcb53f8d78bb77bc1310a9e739","source":{"kind":"arxiv","id":"2506.23688","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.23688","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"arxiv_version","alias_value":"2506.23688v1","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23688","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"pith_short_12","alias_value":"SJQBX2F7B24N","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"pith_short_16","alias_value":"SJQBX2F7B24N6WP4","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"pith_short_8","alias_value":"SJQBX2F7","created_at":"2026-07-05T11:29:26Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2025:SJQBX2F7B24N6WP4PWYZ3O4CPG","target":"record","payload":{"canonical_record":{"source":{"id":"2506.23688","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2025-06-30T10:08:25Z","cross_cats_sorted":[],"title_canon_sha256":"3252bbb8776a568939a4667eef9af3a75a10fc6b2bdd8d8fda2becb446c11fee","abstract_canon_sha256":"5d392fefb2e5e7cfa3543aaf52fe3a2809e0d78d526d90f7d2561e55d956f1b9"},"schema_version":"1.0"},"canonical_sha256":"92601be8bf0eb8df59fc7db19dbb827998b9f6bcb53f8d78bb77bc1310a9e739","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:29:26.387544Z","signature_b64":"b36gAymInjSPaq1sLvVze80o289ProLxCEuNnhPzso3SEkn6lKN2Yrhn6Xk3odS6+xwBDOGbZbWqdogmBRdMCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"92601be8bf0eb8df59fc7db19dbb827998b9f6bcb53f8d78bb77bc1310a9e739","last_reissued_at":"2026-07-05T11:29:26.387056Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:29:26.387056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2506.23688","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":"2026-07-05T11:29:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"KBRiqRa8V3pKIoX6NUW23OhevI5LvSQti1lIWYh/6aERqmgqp+4HO7KIFM6MCW532haN/3fDB+EjCFHbT1+fBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T20:40:44.827911Z"},"content_sha256":"065ea54172195b0febbef77a2ebde3022aae0ea48dc57a63c6f0c9f46c7f0aa1","schema_version":"1.0","event_id":"sha256:065ea54172195b0febbef77a2ebde3022aae0ea48dc57a63c6f0c9f46c7f0aa1"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2025:SJQBX2F7B24N6WP4PWYZ3O4CPG","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"GUSL: A Novel and Efficient Machine Learning Model for Prostate Segmentation on MRI","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Andre Abreu, Catherine Aurelia Christie Alexander, C.-C. Jay Kuo, Chrysostomos Nikias, Giovanni Cacciamani, Inderbir S. Gill, Jiaxin Yang, Jintang Xue, Masatomo Kaneko, Vasileios Magoulianitis, Vinay Duddalwar","submitted_at":"2025-06-30T10:08:25Z","abstract_excerpt":"Prostate and zonal segmentation is a crucial step for clinical diagnosis of prostate cancer (PCa). Computer-aided diagnosis tools for prostate segmentation are based on the deep learning (DL) paradigm. However, deep neural networks are perceived as \"black-box\" solutions by physicians, thus making them less practical for deployment in the clinical setting. In this paper, we introduce a feed-forward machine learning model, named Green U-shaped Learning (GUSL), suitable for medical image segmentation without backpropagation. GUSL introduces a multi-layer regression scheme for coarse-to-fine segme"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23688","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/2506.23688/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":"2026-07-05T11:29:26Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"8jCZoudPgQdYEvPSi4CRBcCdoQBJhMfBtQnkMwBbrP/qkyY2kWPkVZKyi9Gb/JAaBu6YdFW0IUd8Q2dXQeUcBA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-15T20:40:44.828422Z"},"content_sha256":"077aafbec5166281d62b40683644ea8fb07e8590ab5da842ed3c4c35771d89c9","schema_version":"1.0","event_id":"sha256:077aafbec5166281d62b40683644ea8fb07e8590ab5da842ed3c4c35771d89c9"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/bundle.json","state_url":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/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":"2026-08-15T20:40:44Z","links":{"resolver":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG","bundle":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/bundle.json","state":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/state.json","well_known_bundle":"https://pith.science/.well-known/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2025:SJQBX2F7B24N6WP4PWYZ3O4CPG","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":"5d392fefb2e5e7cfa3543aaf52fe3a2809e0d78d526d90f7d2561e55d956f1b9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2025-06-30T10:08:25Z","title_canon_sha256":"3252bbb8776a568939a4667eef9af3a75a10fc6b2bdd8d8fda2becb446c11fee"},"schema_version":"1.0","source":{"id":"2506.23688","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2506.23688","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"arxiv_version","alias_value":"2506.23688v1","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23688","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"pith_short_12","alias_value":"SJQBX2F7B24N","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"pith_short_16","alias_value":"SJQBX2F7B24N6WP4","created_at":"2026-07-05T11:29:26Z"},{"alias_kind":"pith_short_8","alias_value":"SJQBX2F7","created_at":"2026-07-05T11:29:26Z"}],"graph_snapshots":[{"event_id":"sha256:077aafbec5166281d62b40683644ea8fb07e8590ab5da842ed3c4c35771d89c9","target":"graph","created_at":"2026-07-05T11:29:26Z","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/2506.23688/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Prostate and zonal segmentation is a crucial step for clinical diagnosis of prostate cancer (PCa). Computer-aided diagnosis tools for prostate segmentation are based on the deep learning (DL) paradigm. However, deep neural networks are perceived as \"black-box\" solutions by physicians, thus making them less practical for deployment in the clinical setting. In this paper, we introduce a feed-forward machine learning model, named Green U-shaped Learning (GUSL), suitable for medical image segmentation without backpropagation. GUSL introduces a multi-layer regression scheme for coarse-to-fine segme","authors_text":"Andre Abreu, Catherine Aurelia Christie Alexander, C.-C. Jay Kuo, Chrysostomos Nikias, Giovanni Cacciamani, Inderbir S. Gill, Jiaxin Yang, Jintang Xue, Masatomo Kaneko, Vasileios Magoulianitis, Vinay Duddalwar","cross_cats":[],"headline":"","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2025-06-30T10:08:25Z","title":"GUSL: A Novel and Efficient Machine Learning Model for Prostate Segmentation on MRI"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2506.23688","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":"sha256:065ea54172195b0febbef77a2ebde3022aae0ea48dc57a63c6f0c9f46c7f0aa1","target":"record","created_at":"2026-07-05T11:29:26Z","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":"5d392fefb2e5e7cfa3543aaf52fe3a2809e0d78d526d90f7d2561e55d956f1b9","cross_cats_sorted":[],"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2025-06-30T10:08:25Z","title_canon_sha256":"3252bbb8776a568939a4667eef9af3a75a10fc6b2bdd8d8fda2becb446c11fee"},"schema_version":"1.0","source":{"id":"2506.23688","kind":"arxiv","version":1}},"canonical_sha256":"92601be8bf0eb8df59fc7db19dbb827998b9f6bcb53f8d78bb77bc1310a9e739","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"92601be8bf0eb8df59fc7db19dbb827998b9f6bcb53f8d78bb77bc1310a9e739","first_computed_at":"2026-07-05T11:29:26.387056Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T11:29:26.387056Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"b36gAymInjSPaq1sLvVze80o289ProLxCEuNnhPzso3SEkn6lKN2Yrhn6Xk3odS6+xwBDOGbZbWqdogmBRdMCw==","signature_status":"signed_v1","signed_at":"2026-07-05T11:29:26.387544Z","signed_message":"canonical_sha256_bytes"},"source_id":"2506.23688","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:065ea54172195b0febbef77a2ebde3022aae0ea48dc57a63c6f0c9f46c7f0aa1","sha256:077aafbec5166281d62b40683644ea8fb07e8590ab5da842ed3c4c35771d89c9"],"state_sha256":"ecfd6442f89b422f3e7b6bca415f728489db0c7ef9e374764819d96359b662a4"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3Y71nI/MFH3FFo06WbIRFfpp7/OLYnZ/Ozd9zAV6ZW5qzo/aF1LCGUfX1UpamFajuVQgS3IN9pzoZyIXCadkAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-15T20:40:44.833618Z","bundle_sha256":"b9312c72a1ed8d45475dd0c74bb72b6264c6339bd2c66b93fb8eb0be8fdbae93"}}