{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:65F2LH34BWGY6NYGTP2UVIPMNN","short_pith_number":"pith:65F2LH34","schema_version":"1.0","canonical_sha256":"f74ba59f7c0d8d8f37069bf54aa1ec6b6ec34ad16e5b4db1b8cb3fafc3f0a6fe","source":{"kind":"arxiv","id":"2403.15971","version":1},"attestation_state":"computed","paper":{"title":"PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Andre Abreu, C.-C. Jay Kuo, Chrysostomos Nikias, Giovanni Cacciamani, Inderbir S. Gill, Jiaxin Yang, Jintang Xue, Masatomo Kaneko, Vasileios Magoulianitis, Vinay Duddalwar, Yijing Yang","submitted_at":"2024-03-24T00:36:21Z","abstract_excerpt":"Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on deep learning models which have a large size and lack of transparency which is essential for physicians. In this paper, a new data-driven 3D prostate segmentation method on MRI is proposed, named PSHop. Different from deep learning based methods, the core methodology of PSHop is a feed-forward encoder-decoder system based on successive subspace learning (SSL). It consists of two modules: 1) encoder: fine to coarse uns"},"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":"2403.15971","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"eess.IV","submitted_at":"2024-03-24T00:36:21Z","cross_cats_sorted":[],"title_canon_sha256":"d74c3db372297b89939d9eef199d461a57b86f7a7d3ff92fb4f6c4f1f9254d5c","abstract_canon_sha256":"64ba88c68985be4faafc452454b1298cc68dac729f6d8c5db53f32fcddd1b673"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:00:09.145805Z","signature_b64":"5VYigLBnubqaila/Gz3IJezMLkiIDwnqFEc/VfyDxY/Xt1NanXvnG+d9Jrfq2Opw9x4l7SuVQTf54kcKKrxAAA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"f74ba59f7c0d8d8f37069bf54aa1ec6b6ec34ad16e5b4db1b8cb3fafc3f0a6fe","last_reissued_at":"2026-07-05T08:00:09.145325Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:00:09.145325Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"PSHop: A Lightweight Feed-Forward Method for 3D Prostate Gland Segmentation","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":[],"primary_cat":"eess.IV","authors_text":"Andre Abreu, C.-C. Jay Kuo, Chrysostomos Nikias, Giovanni Cacciamani, Inderbir S. Gill, Jiaxin Yang, Jintang Xue, Masatomo Kaneko, Vasileios Magoulianitis, Vinay Duddalwar, Yijing Yang","submitted_at":"2024-03-24T00:36:21Z","abstract_excerpt":"Automatic prostate segmentation is an important step in computer-aided diagnosis of prostate cancer and treatment planning. Existing methods of prostate segmentation are based on deep learning models which have a large size and lack of transparency which is essential for physicians. In this paper, a new data-driven 3D prostate segmentation method on MRI is proposed, named PSHop. Different from deep learning based methods, the core methodology of PSHop is a feed-forward encoder-decoder system based on successive subspace learning (SSL). It consists of two modules: 1) encoder: fine to coarse uns"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.15971","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/2403.15971/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":"2403.15971","created_at":"2026-07-05T08:00:09.145383+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.15971v1","created_at":"2026-07-05T08:00:09.145383+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.15971","created_at":"2026-07-05T08:00:09.145383+00:00"},{"alias_kind":"pith_short_12","alias_value":"65F2LH34BWGY","created_at":"2026-07-05T08:00:09.145383+00:00"},{"alias_kind":"pith_short_16","alias_value":"65F2LH34BWGY6NYG","created_at":"2026-07-05T08:00:09.145383+00:00"},{"alias_kind":"pith_short_8","alias_value":"65F2LH34","created_at":"2026-07-05T08:00:09.145383+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.23688","citing_title":"GUSL: A Novel and Efficient Machine Learning Model for Prostate Segmentation on MRI","ref_index":47,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN","json":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN.json","graph_json":"https://pith.science/api/pith-number/65F2LH34BWGY6NYGTP2UVIPMNN/graph.json","events_json":"https://pith.science/api/pith-number/65F2LH34BWGY6NYGTP2UVIPMNN/events.json","paper":"https://pith.science/paper/65F2LH34"},"agent_actions":{"view_html":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN","download_json":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN.json","view_paper":"https://pith.science/paper/65F2LH34","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.15971&json=true","fetch_graph":"https://pith.science/api/pith-number/65F2LH34BWGY6NYGTP2UVIPMNN/graph.json","fetch_events":"https://pith.science/api/pith-number/65F2LH34BWGY6NYGTP2UVIPMNN/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN/action/timestamp_anchor","attest_storage":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN/action/storage_attestation","attest_author":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN/action/author_attestation","sign_citation":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN/action/citation_signature","submit_replication":"https://pith.science/pith/65F2LH34BWGY6NYGTP2UVIPMNN/action/replication_record"}},"created_at":"2026-07-05T08:00:09.145383+00:00","updated_at":"2026-07-05T08:00:09.145383+00:00"}