  Weakly-supervised learning has become a popular technology in recent years.
In this paper, we propose a novel medical image classification algorithm,
called Weakly-Supervised Generative Adversarial Networks (WSGAN), which only
uses a small number of real images without labels to generate fake images or
mask images to enlarge the sample size of the training set. First, we combine
with MixMatch to generate pseudo labels for the fake images and unlabeled
images to do the classification. Second, contrastive learning and
self-attention mechanism are introduced into the proposed problem to enhance
the classification accuracy. Third, the problem of mode collapse is well
addressed by cyclic consistency loss. Finally, we design global and local
classifiers to complement each other with the key information needed for
classification. The experimental results on four medical image datasets show
that WSGAN can obtain relatively high learning performance by using few labeled
and unlabeled data. For example, the classification accuracy of WSGAN is 11%
higher than that of the second-ranked MIXMATCH with 100 labeled images and 1000
unlabeled images on the OCT dataset. In addition, we also conduct ablation
experiments to verify the effectiveness of our algorithm.
