arXiv AI By Renjie Liang, Zhengkang Fan, Jinqian Pan, Chenkun Sun, Jiang Bian, Russell Terry, Jie Xu

Multi-Granularity 3D Kidney Lesion Characterization from CT Volumes

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arXiv:2606. 04365v1 Announce Type: cross Abstract: Radiology reports describe kidney lesions by type, size, enhancement, and attenuation, yet existing 3D methods predict only at the patient or organ level.

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arXiv AI
Jun 9

Robust Renal Mass Segmentation on CT: A Validation Study of an AI-Based Framework

arXiv:2505. 07573v2 Announce Type: replace-cross Abstract: Renal mass segmentation has important potential to enhance the clinical workflow, especially in settings requiring quantitative assessments.

By Sarah de Boer, Hartmut H\"antze, Kiran Vaidhya Venkadesh, Myrthe A. D. Buser, Gabriel E. Humpire Mamani, Lina Xu, Lisa C. Adams, Jawed Nawabi, Keno K. Bressem, Bram van Ginneken, Mathias Prokop, Alessa Hering
arXiv Machine Learning
Jun 16

A Multi-Center Benchmark for Abdominal Disease Diagnosis and Report Generation from Non-Contrast CT

arXiv:2606. 16991v1 Announce Type: cross Abstract: Multiphasic contrast-enhanced CT (CECT) is widely used for abdominal lesion characterization, yet it carries inherent risks of contrast-induced nephropathy, escalates acquisition burden, and heavily contributes to radiologist workload.

By Mariam Elbakry, Aliaa Sayed Sheha, Salma Hassan Tantawy, Aya Yassin, Concetto Spampinato, Karim Lekadir, Xiaomeng Li, Marawan Elbatel
arXiv AI
Jun 30

Evidence-Based Text-Conditioned 3D CT Synthesis for Ovarian Cancer

arXiv:2606. 28980v1 Announce Type: cross Abstract: Ovarian cancer is frequently diagnosed at an advanced stage, making preoperative contrast-enhanced computed tomography (CT) central to staging and surgical planning; yet the scarcity of annotated imaging data, compounded by privacy regulations, limits the development of generalizable computational models in this domain.

By Francesca Pia Panaccione, Eugenio Lomurno, Francesca Fati, Carlotta Pecchiari, Marina Rosanu, Luigi De Vitis, Lucia Ribero, Gabriella Schivardi, Giovanni Damiano Aletti, Nicoletta Colombo, Maria Francesca Spadea, Francesco Multinu, Matteo Matteucci, Elena De Momi
arXiv Machine Learning
Jul 2

Foundation Models vs. Radiomics for Lung Computed Tomography: A Benchmark of Feature Extractors, Classification Heads, and Segmentation Choices

arXiv:2607. 01001v1 Announce Type: cross Abstract: Radiomics is the established approach for CT-based lung cancer phenotyping, yet comparisons with foundation models rarely isolate contributions of feature extractor, classification head, and segmentation choice, or test cross-cohort robustness.

By Nils Neukirch, Martin Maurer, Nils Strodthoff