Adversarial Pseudo Healthy Synthesis Needs Pathology Factorization. Pseudo healthy synthesis, i.e. the creation of a subjectspecific healthyu0027 image from a pathological one, could be helpful in tasks such as anomaly detection, understanding changes induced by pathology and disease or even as data augmentation. We treat this task as a factor decomposition problem: we aim to separate what appears to be healthy and where disease is (as a map). The two factors are then recombined (by a network) to reconstruct the input disease image. We train our models in an adversarial way using either paired or unpaired settings, where we pair disease images and maps (as segmentation masks) when available. We quantitatively evaluate the quality of pseudo healthy images. We show in a series of experiments, performed in ISLES and BraTS datasets, that our method is better than conditional GAN and CycleGAN, highlighting challenges in using adversarial methods in the image translation task of pseudo healthy image generation.

Generative Image Translation for Data Augmentation of Bone Lesion Pathology Insufficient training data and severe class imbalance are often limiting factors when developing machine learning models for the classification of rare diseases. In this work, we address the problem of classifying bone lesions from Xray images by increasing the small number of positive samples in the training set. We propose a generative data augmentation approach based on a cycleconsistent generative adversarial network that synthesizes bone lesions on images without pathology. We pose the generative task as an imagepatch translation problem that we optimize specifically for distinct bones (humerus, tibia, femur). In experimental results, we confirm that the described method mitigates the class imbalance problem in the binary classification task of bone lesion detection. We show that the augmented training sets enable the training of superior classifiers achieving better performance on a heldout test set. Additionally, we demonstrate the feasibility of transfer learning and apply a generative model that was trained on one body part to another.