DAE-CNN: Exploiting and disentangling contrast agent effects for breast lesions classification in DCE-MRI

Convolutional Neural Networks (CNNs) are opening for unprecedented scenarios in fields where designing effective f eatures is tedious even for domain experts. This is the case of medical imaging, i.e. procedures acquiring images of a human body interior for clinical proposes. Despite promising, we argue that CNNs naive use may not be effective since "medical images are more than pictures". A notable example is breast Dynamic Contrast Enhanced-Magnetic Resonance Imaging (DCE-MRI), in which the kinetic of the injected Contrast Agent (CA) is crucial for lesion classification purposes. Therefore, in this work we introduce a new GAN like approach designed to simultaneously learn how to disentangle the CA effects from all the other image components while performing the lesion classification: the generator is an intrinsic Deforming Autoencoder (DAE), while the discriminator is a CNN. We compared the performance of the proposed approach against some literature proposals (both classical and CNN based) using patient-wise cross-validation. Finally, for the sake of completeness, we also analyzed the impact of variations in some key aspect of the proposed solution. Results not only show the effectiveness of our approach (+8% AUC w.r.t. the runner-up) but also confirm that all the approach's components effectively contribute to the solution. (C) 2021 Elsevier B.V. All rights reserved.