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.

SemiSupervised AttentionGuided CycleGAN for Data Augmentation on Medical Images Recently, deep learning methods, in particular, convolutional neural networks (CNNs), have made a massive breakthrough in computer vision. And a big amount of annotated data is the essential cornerstone to reach this success. However, in the medical domain, it is usually difficult (and sometimes even impossible) to get sufficient data for some specific learning tasks. Consequently, in this work, a novel data augmentation solution, called semisupervised attentionguided CycleGAN (SSACycleGAN) is proposed to resolve this problem. Specifically, a cycleconsistency GANsbased model is first proposed to generate synthetic tumor (resp., normal) images from normal (resp., tumor) images. Then, a semisupervised attention module is further proposed to enhance the modelu0027s capability in learning the important details of the training images, which in turns help the generated synthetic images become more realistic. To verify its effectiveness, experimental studies are conducted on three medical image datasets with limited amounts of MRI images, and the proposed SSACycleGAN is applied to generate synthetic tumor and normal MRI images for data augmentation. Experimental results show that i) SSACycleGAN can add (resp., remove) tumor lesions on (resp., from) the original normal (resp., tumor) images and generate very realistic synthetic tumor (resp. normal) images; and ii) in the ResNet18based MRI image classification tasks based on these datasets, data augmentation using SSACycleGAN achieves much better classification performances than the classic data augmentation methods.