{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:7YTYNGQBMHNRPNSFBWJRLHLSQG","short_pith_number":"pith:7YTYNGQB","schema_version":"1.0","canonical_sha256":"fe27869a0161db17b6450d93159d72818038390ed737d9e0a02acc4f77f59d31","source":{"kind":"arxiv","id":"2507.00377","version":1},"attestation_state":"computed","paper":{"title":"MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guibo Luo, Jianhao Xie, Yuesheng Zhu, Zhenyu Weng, Ziang Zhang","submitted_at":"2025-07-01T02:22:32Z","abstract_excerpt":"Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion models provide a potential solution by generating synthetic images, their effectiveness in medical imaging remains constrained due to their reliance on large-scale medical datasets and the need for higher image quality. To address these challenges, we present MedDiff-FT, a controllable medical image generation method that fine-tunes a diffusion foundation model to produce medical images with structural dependency and domain specificity in a data-ef"},"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":"2507.00377","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2025-07-01T02:22:32Z","cross_cats_sorted":[],"title_canon_sha256":"b950ebcc9e460c5e265ee44d74b9eefe55ab2810e9d4e8551fb6f4f4b179bf55","abstract_canon_sha256":"4f0c6139d15657cf2bb2edb9c99148c6bef5648f50191d5177886f3e09efa62d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:30:07.767629Z","signature_b64":"2xLtTLL5eBUPNWmPVx8RVMwYw6CUWpV1GHxZc2xhpjr9a3j8xz1bKKStYnng1sYK4d9buC2UQDRVNxll+uduCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"fe27869a0161db17b6450d93159d72818038390ed737d9e0a02acc4f77f59d31","last_reissued_at":"2026-07-05T11:30:07.767116Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:30:07.767116Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"MedDiff-FT: Data-Efficient Diffusion Model Fine-tuning with Structural Guidance for Controllable Medical Image Synthesis","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Guibo Luo, Jianhao Xie, Yuesheng Zhu, Zhenyu Weng, Ziang Zhang","submitted_at":"2025-07-01T02:22:32Z","abstract_excerpt":"Recent advancements in deep learning for medical image segmentation are often limited by the scarcity of high-quality training data.While diffusion models provide a potential solution by generating synthetic images, their effectiveness in medical imaging remains constrained due to their reliance on large-scale medical datasets and the need for higher image quality. To address these challenges, we present MedDiff-FT, a controllable medical image generation method that fine-tunes a diffusion foundation model to produce medical images with structural dependency and domain specificity in a data-ef"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2507.00377","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/2507.00377/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":"2507.00377","created_at":"2026-07-05T11:30:07.767176+00:00"},{"alias_kind":"arxiv_version","alias_value":"2507.00377v1","created_at":"2026-07-05T11:30:07.767176+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2507.00377","created_at":"2026-07-05T11:30:07.767176+00:00"},{"alias_kind":"pith_short_12","alias_value":"7YTYNGQBMHNR","created_at":"2026-07-05T11:30:07.767176+00:00"},{"alias_kind":"pith_short_16","alias_value":"7YTYNGQBMHNRPNSF","created_at":"2026-07-05T11:30:07.767176+00:00"},{"alias_kind":"pith_short_8","alias_value":"7YTYNGQB","created_at":"2026-07-05T11:30:07.767176+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2512.10877","citing_title":"Guided Transfer Learning for Discrete Diffusion Models","ref_index":1,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG","json":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG.json","graph_json":"https://pith.science/api/pith-number/7YTYNGQBMHNRPNSFBWJRLHLSQG/graph.json","events_json":"https://pith.science/api/pith-number/7YTYNGQBMHNRPNSFBWJRLHLSQG/events.json","paper":"https://pith.science/paper/7YTYNGQB"},"agent_actions":{"view_html":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG","download_json":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG.json","view_paper":"https://pith.science/paper/7YTYNGQB","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2507.00377&json=true","fetch_graph":"https://pith.science/api/pith-number/7YTYNGQBMHNRPNSFBWJRLHLSQG/graph.json","fetch_events":"https://pith.science/api/pith-number/7YTYNGQBMHNRPNSFBWJRLHLSQG/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG/action/timestamp_anchor","attest_storage":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG/action/storage_attestation","attest_author":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG/action/author_attestation","sign_citation":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG/action/citation_signature","submit_replication":"https://pith.science/pith/7YTYNGQBMHNRPNSFBWJRLHLSQG/action/replication_record"}},"created_at":"2026-07-05T11:30:07.767176+00:00","updated_at":"2026-07-05T11:30:07.767176+00:00"}