Learning Representations by Maximizing Mutual Information Across Views for Medical Image Segmentation

We propose a method that leverages multiple identical network structures to generate and process diverse augmented views of the same medical image sample. By employing contrastive learning, we maximize mutual information among features extracted from different views, ensuring the networks learn robust and high-level semantic representations. Results from testing on four public and one private endoscopic surgical tool segmentation datasets indicate that the proposed method outperformed state-of-the-art semi-supervised and fully supervised segmentation methods. After trained by 5 \(\%\) labeled training data, the proposed method achieved an improvement of 11.5 \(\%\) , 8.4 \(\%\) , 6.5 \(\%\) , and 5.8 \(\%\) on RoboTool, Kvasir-instrument, ART-NET, and FEES, respectively. Ablation studies were also performed to measure the effectiveness of each proposed module. Code is available at Mutual-Exemplar .