Abstract:
Semi-supervised learning has made significant strides in the medical domain
since it alleviates the heavy burden of collecting abundant pixel-wise
annotated data for semantic segmentation tasks. Existing semi-supervised
approaches enhance the ability to extract features from unlabeled data with
prior knowledge obtained from limited labeled data. However, due to the
scarcity of labeled data, the features extracted by the models are limited in
supervised learning, and the quality of predictions for unlabeled data also
cannot be guaranteed. Both will impede consistency training. To this end, we
proposed a novel uncertainty-aware scheme to make models learn regions
purposefully. Specifically, we employ Monte Carlo Sampling as an estimation
method to attain an uncertainty map, which can serve as a weight for losses to
force the models to focus on the valuable region according to the
characteristics of supervised learning and unsupervised learning.
Simultaneously, in the backward process, we joint unsupervised and supervised
losses to accelerate the convergence of the network via enhancing the gradient
flow between different tasks. Quantitatively, we conduct extensive experiments
on three challenging medical datasets. Experimental results show desirable
improvements to state-of-the-art counterparts.