2 citations · 4 across the 8 of their papers we have counts for
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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…
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…
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…
Segmentation of Planning Target Volume in CT Series for Total Marrow Irradiation Using U-Net
Ricardo Coimbra Brioso, Damiano Dei, Ciro Franzese +4
Radiotherapy (RT) is a key component in the treatment of various cancers, including Acute Lymphocytic Leukemia (ALL) and Acute Myelogenous Leukemia (AML). Precise delineation of or…