{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:SJQBX2F7B24N6WP4PWYZ3O4CPG","short_pith_number":"pith:SJQBX2F7","schema_version":"1.0","canonical_sha256":"92601be8bf0eb8df59fc7db19dbb827998b9f6bcb53f8d78bb77bc1310a9e739","source":{"kind":"arxiv","id":"2506.23688","version":1},"attestation_state":"computed","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"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"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"},"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"},"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"},"aliases":[{"alias_kind":"arxiv","alias_value":"2506.23688","created_at":"2026-07-05T11:29:26.387109+00:00"},{"alias_kind":"arxiv_version","alias_value":"2506.23688v1","created_at":"2026-07-05T11:29:26.387109+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2506.23688","created_at":"2026-07-05T11:29:26.387109+00:00"},{"alias_kind":"pith_short_12","alias_value":"SJQBX2F7B24N","created_at":"2026-07-05T11:29:26.387109+00:00"},{"alias_kind":"pith_short_16","alias_value":"SJQBX2F7B24N6WP4","created_at":"2026-07-05T11:29:26.387109+00:00"},{"alias_kind":"pith_short_8","alias_value":"SJQBX2F7","created_at":"2026-07-05T11:29:26.387109+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG","json":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG.json","graph_json":"https://pith.science/api/pith-number/SJQBX2F7B24N6WP4PWYZ3O4CPG/graph.json","events_json":"https://pith.science/api/pith-number/SJQBX2F7B24N6WP4PWYZ3O4CPG/events.json","paper":"https://pith.science/paper/SJQBX2F7"},"agent_actions":{"view_html":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG","download_json":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG.json","view_paper":"https://pith.science/paper/SJQBX2F7","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2506.23688&json=true","fetch_graph":"https://pith.science/api/pith-number/SJQBX2F7B24N6WP4PWYZ3O4CPG/graph.json","fetch_events":"https://pith.science/api/pith-number/SJQBX2F7B24N6WP4PWYZ3O4CPG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/action/storage_attestation","attest_author":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/action/author_attestation","sign_citation":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/action/citation_signature","submit_replication":"https://pith.science/pith/SJQBX2F7B24N6WP4PWYZ3O4CPG/action/replication_record"}},"created_at":"2026-07-05T11:29:26.387109+00:00","updated_at":"2026-07-05T11:29:26.387109+00:00"}