Data Augmentation in Logit Space for Medical Image Classification with Limited Training Data.

Successful application of deep learning often depends on large amount of training data. However in practical medical image analysis, available training data are often limited, often causing over-fitting during model training. In this paper, a novel data augmentation method is proposed to effectively alleviate the over-fitting issue, not in the input space but in the logit space. This is achieved by perturbing the logit vector of each training data within the neighborhood of the logit vector in the logit space, where the size of neighborhood can be automatically and adaptively estimated for each training data over training stages. The augmentations in the logit space may implicitly represent various transformations or augmentations in the input space, and therefore can help train a more generalizable classifier. Extensive evaluations on three small medical image datasets and multiple classifier backbones consistently support the effectiveness of the proposed method.