Abstract
Case-based explanations are an intuitive method to gain insight into the decision-making process of deep learning models in clinical contexts. However, medical images cannot be shared as explanations due to privacy concerns. To address this problem, we propose a novel method for disentangling identity and medical characteristics of images and apply it to anonymize medical images. The disentanglement mechanism replaces some feature vectors in an image while ensuring that the remaining features are preserved, obtaining independent feature vectors that encode the images' identity and medical characteristics. We also propose a model to manufacture synthetic privacy-preserving identities to replace the original image's identity and achieve anonymization. The models are applied to medical and biometric datasets, demonstrating their capacity to generate realistic-looking anonymized images that preserve their original medical content. Additionally, the experiments show the network's inherent capacity to generate counterfactual images through the replacement of medical features.

Abstract
Breast cancer locoregional treatment causes alterations to the physical aspect of the breast, often negatively impacting the self-esteem of patients unaware of the possible aesthetic outcomes of those treatments. To improve patients' self-esteem and enable a more informed choice of treatment when multiple options are available, the possibility to predict how the patient might look like after surgery would be of invaluable help. However, no work has been proposed to predict the aesthetic outcomes of breast cancer treatment. As a first step, we compare traditional computer vision and deep learning approaches to reproduce asymmetries of post-operative patients on pre-operative breast images. The results suggest that the traditional approach is better at altering the contour of the breast. In contrast, the deep learning approach succeeds in realistically altering the position and direction of the nipple. © 2024 IEEE.