  A common problem with segmentation of medical images using neural networks is
the difficulty to obtain a significant number of pixel-level annotated data for
training. To address this issue, we proposed a semi-supervised segmentation
network based on contrastive learning. In contrast to the previous
state-of-the-art, we introduce Min-Max Similarity (MMS), a contrastive learning
form of dual-view training by employing classifiers and projectors to build
all-negative, and positive and negative feature pairs, respectively, to
formulate the learning as solving a MMS problem. The all-negative pairs are
used to supervise the networks learning from different views and to capture
general features, and the consistency of unlabeled predictions is measured by
pixel-wise contrastive loss between positive and negative pairs. To
quantitatively and qualitatively evaluate our proposed method, we test it on
four public endoscopy surgical tool segmentation datasets and one cochlear
implant surgery dataset, which we manually annotated. Results indicate that our
proposed method consistently outperforms state-of-the-art semi-supervised and
fully supervised segmentation algorithms. And our semi-supervised segmentation
algorithm can successfully recognize unknown surgical tools and provide good
predictions. Also, our MMS approach could achieve inference speeds of about 40
frames per second (fps) and is suitable to deal with the real-time video
segmentation.
