  While making a tremendous impact in various fields, deep neural networks
usually require large amounts of labeled data for training which are expensive
to collect in many applications, especially in the medical domain. Unlabeled
data, on the other hand, is much more abundant. Semi-supervised learning
techniques, such as co-training, could provide a powerful tool to leverage
unlabeled data. In this paper, we propose a novel framework, uncertainty-aware
multi-view co-training (UMCT), to address semi-supervised learning on 3D data,
such as volumetric data from medical imaging. In our work, co-training is
achieved by exploiting multi-viewpoint consistency of 3D data. We generate
different views by rotating or permuting the 3D data and utilize asymmetrical
3D kernels to encourage diversified features in different sub-networks. In
addition, we propose an uncertainty-weighted label fusion mechanism to estimate
the reliability of each view's prediction with Bayesian deep learning. As one
view requires the supervision from other views in co-training, our
self-adaptive approach computes a confidence score for the prediction of each
unlabeled sample in order to assign a reliable pseudo label. Thus, our approach
can take advantage of unlabeled data during training. We show the effectiveness
of our proposed semi-supervised method on several public datasets from medical
image segmentation tasks (NIH pancreas & LiTS liver tumor dataset). Meanwhile,
a fully-supervised method based on our approach achieved state-of-the-art
performances on both the LiTS liver tumor segmentation and the Medical
Segmentation Decathlon (MSD) challenge, demonstrating the robustness and value
of our framework, even when fully supervised training is feasible.
