Self-distillation Augmented Masked Autoencoders for Histopathological Image Classification
This work addresses the challenge of false negatives in contrastive learning for histopathological image analysis, offering a more appropriate pre-training method for medical imaging applications.
The paper tackled the problem of improving self-supervised learning for histopathological image analysis by proposing a self-distillation augmented masked autoencoder (SD-MAE) that enhances encoder representational capacity, resulting in highly competitive performance in classification, segmentation, and detection tasks compared to other SSL methods.
Self-supervised learning (SSL) has drawn increasing attention in histopathological image analysis in recent years. Compared to contrastive learning which is troubled with the false negative problem, i.e., semantically similar images are selected as negative samples, masked autoencoders (MAE) building SSL from a generative paradigm is probably a more appropriate pre-training. In this paper, we introduce MAE and verify the effect of visible patches for histopathological image understanding. Moreover, a novel SD-MAE model is proposed to enable a self-distillation augmented MAE. Besides the reconstruction loss on masked image patches, SD-MAE further imposes the self-distillation loss on visible patches to enhance the representational capacity of the encoder located shallow layer. We apply SD-MAE to histopathological image classification, cell segmentation and object detection. Experiments demonstrate that SD-MAE shows highly competitive performance when compared with other SSL methods in these tasks.