{"ID":3053334,"CreatedAt":"2026-06-04T04:41:36.695875263Z","UpdatedAt":"2026-06-06T02:25:55.484849507Z","DeletedAt":null,"paper_url":"https://arxiv.org/abs/2606.04365","arxiv_id":"2606.04365","title":"Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes","abstract":"Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attributes. We curated 2,619 CT volumes from 788 patients at one academic medical center, with multi-granularity side- and per-lesion labels, and used KiTS23 (489 cases) for zero-shot external validation. We propose \\textbf{LesionDETR}, a DETR-style architecture with size-distance Hungarian matching and a hierarchical loss that aggregates per-slot outputs to side-level objectives. Across four input representations and six encoder initializations, two design choices dominate: a segmentation mask as an input channel, and same-domain abdominal pretraining (SuPreM); generic large-corpus pretraining is no better than random initialization. LesionDETR reaches bilateral side-level abnormality AUC $0.799 \\pm 0.009$ on UF-Health and $0.817 \\pm 0.072$ on KiTS23. A count-conditioned variant reaches per-lesion mAP $0.190 \\pm 0.083$ on cystic lesions; rare solid-lesion AP stays at the noise floor, pointing to targeted data collection, not architecture, as the next bottleneck. The framework yields verified per-lesion predictions for downstream structured report generation.","short_abstract":"Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level. We reformulate kidney CT characterization as a per-lesion set-prediction task: one model emits a variable number of lesions per kidney, each with four clinical attri...","url_abs":"https://arxiv.org/abs/2606.04365","url_pdf":"https://arxiv.org/pdf/2606.04365v1","authors":"[\"Renjie Liang\",\"Zhengkang Fan\",\"Jinqian Pan\",\"Chenkun Sun\",\"Jiang Bian\",\"Russell Terry\",\"Jie Xu\"]","published":"2026-06-03T02:28:57Z","proceeding":"cs.CV","tasks":"[\"cs.CV\",\"cs.AI\"]","methods":"[\"Generative Adversarial Network\"]","has_code":false}