Two fundamentally different approaches have been proposed recently for the classification of breast lesions on diffusion-weighted MRI Images: “Radiomics” extracts quantitative parameters by fitting a biophysical model to the q-space signal and subsequently computes handcrafted features to feed a classifier. Convolutional neural networks on the other hand autonomously learn all processing components in an end-to-end training. To date it is unclear how the two methods compare with respect to overall performance, complementary value of features and combinability. We address these open research questions and propose a combined model that significantly outperforms the two standalone approaches.
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