Integrate Memory Efficiency Methods for Self-Supervised Learning on Pathological Image Analysis.

Contrastive learning, a recent family of self-supervised learning, leverages pathological image analysis by learning from large-scale unannotated data. However, the state-of-the-art contrastive learning methods (e.g., SimCLR, BYOL) are typically limited by the more expensive computational hardware (with large GPU memory) as compared with traditional supervised learning approaches in achieving large training batch size. Fortunately, recent advances in the machine learning community provide multiple approaches to reduce GPU memory usage, such as (1) activation compressed training, (2) In-place activation, and (3) mixed precision training. Yet, such approaches are currently deployed independently without systematical assessments for contrastive learning. In this work, we applied these memory-efficient approaches into a self-supervised framework. The contribution of this paper is three-fold: (1) We combined previously independent GPU memory-efficient methods with self-supervised learning framework; (2) Our experiments are to maximize the memory efficiency via limited computational resources (a single GPU); (3) The self-supervised learning framework with GPU memory-efficient method allows a single GPU to triple the batch size that typically requires three GPUs. From the experimental results, contrastive learning model with larger batch size leads to higher accuracy enabled by GPU memory-efficient method on single GPU.