Four Public Datasets for Explainable Medical Image Classifications

Modern deep neural networks are overparameterized and thus require data augmentation techniques to prevent over-fitting and improve generalization ability. Generative adversarial networks (GANs) are famous for generating visually realistic images. However, the generated images lack diversity and have uncertain class labels. On the other hand, recent methods mix labels proportionally to the salient region. As the main diagnostic information is not always contained within the salient region, we argue that methods along this line can cause label mismatch issues in medical image classifications. Therefore, we propose VariMix, which exploits an absolute difference map (ADM) to address the label mismatching of mixed medical images. The VariMix generates ADM using the image-to-image (I2I) GAN across multiple classes and allows for bidirectional mixing operations between the training samples.  We collect four public medical image datasets for automatic medical image classifications: Breast Ultrasound datset, Chest X-Ray Images (CXR) dataset, Eye Disease Retinal Images (Retinal) dataset, Maternal-fetal ultrasound dataset. Extensive experiments prove the superiority of VariMix compared with the existing GAN-based and Mixupbased augmentation methods on four public datasets using Swin Transformer V2 and ConvNeXt architectures. Furthermore, by projecting the source image to the hyperplane of the support vector machine, the proposed I2I GAN can generate hyperplane difference maps (HDM) between the source image and the hyperplane image, demonstrating its ability to interpret medical image classifications.