An original voxel-wise supervised analysis of tumors with multimodal radiomics to highlight predictive biological patterns

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Escobar, Thibault | Vauclin, Sebastien | Orlhac, Fanny | Nioche, Christophe | Pineau, Pascal | Buvat, Irene

Edité par CCSD -

International audience. Translational applications of predictive and prognostic image-based learning models are challenging due to their lack of interpretability. When using deep learning, Class Activation Maps (CAM) give information about the regions driving the models. Yet, due to the high-level abstraction of deep features, deep CAM are difficult to interpret. We propose a method that combines the interpretability of handcrafted radiomics with a voxel-wise analysis and facilitates the biological interpretation of models.

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