{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:2YCTW7VHTLX627ZI4QGQIFTFA5","short_pith_number":"pith:2YCTW7VH","schema_version":"1.0","canonical_sha256":"d6053b7ea79aefed7f28e40d0416650769c9e7113527b7f996a933fc535bcaa3","source":{"kind":"arxiv","id":"2310.12868","version":2},"attestation_state":"computed","paper":{"title":"DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alpay Medetalibeyoglu, Bin Wang, Debesh Jha, Elif Keles, Gorkem Durak, Lanhong Yao, Ulas Bagci, Zheyuan Zhang","submitted_at":"2023-10-19T16:18:02Z","abstract_excerpt":"Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called DiffBoost. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve th"},"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":"2310.12868","kind":"arxiv","version":2},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2023-10-19T16:18:02Z","cross_cats_sorted":[],"title_canon_sha256":"74f03006a34b526ea80cdb7313d28023427f25e80347789e5ced4b9ecc351f33","abstract_canon_sha256":"4f644139e17b94ad263ecc2b9aea1edffbac2c452bde16454c8ce3bc53e70ca8"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T09:49:00.792930Z","signature_b64":"4hSoIlss5/2ZwSWoCoOlouNbkoEDAtD4oGmoVXmAYTiI+pzJARrePOlOsQNiIqBlBOJHu2T9lqVOfoBsdHHkDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d6053b7ea79aefed7f28e40d0416650769c9e7113527b7f996a933fc535bcaa3","last_reissued_at":"2026-07-05T09:49:00.792386Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T09:49:00.792386Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"DiffBoost: Enhancing Medical Image Segmentation via Text-Guided Diffusion Model","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Alpay Medetalibeyoglu, Bin Wang, Debesh Jha, Elif Keles, Gorkem Durak, Lanhong Yao, Ulas Bagci, Zheyuan Zhang","submitted_at":"2023-10-19T16:18:02Z","abstract_excerpt":"Large-scale, big-variant, high-quality data are crucial for developing robust and successful deep-learning models for medical applications since they potentially enable better generalization performance and avoid overfitting. However, the scarcity of high-quality labeled data always presents significant challenges. This paper proposes a novel approach to address this challenge by developing controllable diffusion models for medical image synthesis, called DiffBoost. We leverage recent diffusion probabilistic models to generate realistic and diverse synthetic medical image data that preserve th"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2310.12868","kind":"arxiv","version":2},"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/2310.12868/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":"2310.12868","created_at":"2026-07-05T09:49:00.792449+00:00"},{"alias_kind":"arxiv_version","alias_value":"2310.12868v2","created_at":"2026-07-05T09:49:00.792449+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2310.12868","created_at":"2026-07-05T09:49:00.792449+00:00"},{"alias_kind":"pith_short_12","alias_value":"2YCTW7VHTLX6","created_at":"2026-07-05T09:49:00.792449+00:00"},{"alias_kind":"pith_short_16","alias_value":"2YCTW7VHTLX627ZI","created_at":"2026-07-05T09:49:00.792449+00:00"},{"alias_kind":"pith_short_8","alias_value":"2YCTW7VH","created_at":"2026-07-05T09:49:00.792449+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2505.05573","citing_title":"Prompt to Polyp: Medical Text-Conditioned Image Synthesis with Diffusion Models","ref_index":80,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5","json":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5.json","graph_json":"https://pith.science/api/pith-number/2YCTW7VHTLX627ZI4QGQIFTFA5/graph.json","events_json":"https://pith.science/api/pith-number/2YCTW7VHTLX627ZI4QGQIFTFA5/events.json","paper":"https://pith.science/paper/2YCTW7VH"},"agent_actions":{"view_html":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5","download_json":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5.json","view_paper":"https://pith.science/paper/2YCTW7VH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2310.12868&json=true","fetch_graph":"https://pith.science/api/pith-number/2YCTW7VHTLX627ZI4QGQIFTFA5/graph.json","fetch_events":"https://pith.science/api/pith-number/2YCTW7VHTLX627ZI4QGQIFTFA5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5/action/storage_attestation","attest_author":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5/action/author_attestation","sign_citation":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5/action/citation_signature","submit_replication":"https://pith.science/pith/2YCTW7VHTLX627ZI4QGQIFTFA5/action/replication_record"}},"created_at":"2026-07-05T09:49:00.792449+00:00","updated_at":"2026-07-05T09:49:00.792449+00:00"}