  Contrastive learning is a key technique of modern self-supervised learning.
The broader accessibility of earlier approaches is hindered by the need of
heavy computational resources (e.g., at least 8 GPUs or 32 TPU cores), which
accommodate for large-scale negative samples or momentum. The more recent
SimSiam approach addresses such key limitations via stop-gradient without
momentum encoders. In medical image analysis, multiple instances can be
achieved from the same patient or tissue. Inspired by these advances, we
propose a simple triplet representation learning (SimTriplet) approach on
pathological images. The contribution of the paper is three-fold: (1) The
proposed SimTriplet method takes advantage of the multi-view nature of medical
images beyond self-augmentation; (2) The method maximizes both intra-sample and
inter-sample similarities via triplets from positive pairs, without using
negative samples; and (3) The recent mix precision training is employed to
advance the training by only using a single GPU with 16GB memory. By learning
from 79,000 unlabeled pathological patch images, SimTriplet achieved 10.58%
better performance compared with supervised learning. It also achieved 2.13%
better performance compared with SimSiam. Our proposed SimTriplet can achieve
decent performance using only 1% labeled data. The code and data are available
at https://github.com/hrlblab/SimTriple.
