Improving dense pixelwise prediction of epithelial density using unsupervised data augmentation for consistency regularization

Abstract

Although the amount of medical data keeps increasing, data annotations are scarce and often very difficult to obtain. It is even harder for the case that involves multi-modal imaging or data such as radiology-pathology correlation. In this regard, semi-supervised learning has the potential to leverage unlabeled data for improved medical image analysis. Herein, we propose a semi-supervised learning framework for the dense pixelwise prediction in multi-parametric magnetic resonance imaging (mpMRI). The proposed method predicts the epithelial density in mpMRI per-voxel basis. The ground truth annotations are only obtainable from the corresponding pathology images, which are often unavailable for mpMRI. Introducing unsupervised data augmentation and supervised training signal annealing strategies during training, the proposed method utilizes both labeled and unlabeled mpMRI in an efficient and effective manner. The experimental results demonstrate that the proposed framework is effective in improving the stability and accuracy of the density prediction. The proposed framework achieves the mean absolute error of 6.493, compared to 7.353 by the supervised learning counterpart, outperforming other competing methods. The results suggest that the semi-supervised learning framework could aid in resolving the scarcity of medical data and annotations, in particular for radiology-pathology correlation.