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cs.CV2026
Budget-Aware Uncertainty for Radiotherapy Segmentation QA Using nnU-Net
Ricardo Coimbra Brioso, Lorenzo Mondo, Damiano Dei +4
Accurate delineation of the Clinical Target Volume (CTV) is essential for radiotherapy planning, yet remains time-consuming and difficult to assess, especially for complex treatmen…
cs.CV2026
Efficient KernelSHAP Explanations for Patch-based 3D Medical Image Segmentation
Ricardo Coimbra Brioso, Giulio Sichili, Damiano Dei +4
Perturbation-based explainability methods such as KernelSHAP provide model-agnostic attributions but are typically impractical for patch-based 3D medical image segmentation due to…
cs.CV2024
Deep Learning-Based Auto-Segmentation of Planning Target Volume for Total Marrow and Lymph Node Irradiation
Ricardo Coimbra Brioso, Damiano Dei, Nicola Lambri +3
In order to optimize the radiotherapy delivery for cancer treatment, especially when dealing with complex treatments such as Total Marrow and Lymph Node Irradiation (TMLI), the acc…