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eess.IV2022
BayesNetCNN: incorporating uncertainty in neural networks for image-based classification tasks
Matteo Ferrante, Tommaso Boccato, Nicola Toschi
The willingness to trust predictions formulated by automatic algorithms is key in a vast number of domains. However, a vast number of deep architectures are only able to formulate…
eess.IV2022★ 1 cited
Application of the nnU-Net for automatic segmentation of lung lesion on CT images, and implication on radiomic models
Matteo Ferrante, Lisa Rinaldi, Francesca Botta +17
Lesion segmentation is a crucial step of the radiomic workflow. Manual segmentation requires long execution time and is prone to variability, impairing the realisation of radiomic…