Student Collaboration Improves Self-Supervised Learning: Dual-Loss Adaptive Masked Autoencoder for Brain Cell Image Analysis

Self-supervised learning leverages the underlying data structure as the source of the supervisory signal without the need for human annotation effort. This approach offers a practical solution to learning with a large amount of biomedical data and limited annotation. Unlike other studies exploiting data via multi-view (e.g., augmented images), this study presents a self-supervised Dual-Loss Adaptive Masked Autoencoder (DAMA) algorithm established from the viewpoint of the information theory. Specifically, our objective function maximizes the mutual information by minimizing the conditional entropy in pixel-level reconstruction and feature-level regression. We further introduce an adaptive mask sampling strategy to maximize mutual information. We conduct extensive experiments on brain cell images to validate the proposed method. DAMA significantly outperforms both state-of-the-art self-supervised and supervised methods on brain cells data and demonstrates competitive result on ImageNet-1k. Code: https://github.com/hula-ai/DAMA