Semi-supervised Learning via Improved Teacher-Student Network for Robust 3D Reconstruction of Stereo Endoscopic Image

nique for varied surgical systems, e.g., medical droids, navigations, etc., suffers from severe overfitting problems due to scarce labels. Semi-supervised learning based on Teacher-Student Network (TSN) is a potential solution, which utilizes a supervised teacher model trained on available labeled data to teach a student model on all images via assigning them pseudo labels. However, TSN often faces a dilemma: if given only few labeled endoscope images, the teacher model will be trained to be defective and induce high-noised pseudo labels, degrading the student model significantly. To solve this, we propose an improved TSN for a robust 3D reconstruction of stereo endoscope image. Specifically, two novel modules are introduced: 1) a semi-supervised teacher model based on adversarial learning to produce mostly correct pseudo labels by forcing a consistency in predictions for both labeled and unlabeled data, and 2) a confidence network to further filter out noisy pseudo labels by estimating a confidence for each prediction of the teacher model. By doing so, the student model is able to distill knowledge from more accurate and noiseless pseudo labels, thus achieving improved performance. Experimental results on two public datasets show that our improved TSN achieves a superior performance than the state-of-the-arts by reducing the averaged disparity error by at least 13.5%.