Transformation-Consistent Semi-Supervised Learning for Prostate CT Radiotherapy

Deep supervised models often require a large amount of labelled data, which is difficult to obtain in the medical domain. Therefore, semi-supervised learning (SSL) has been an active area of research due to its promise to minimize training costs by leveraging unlabelled data. Previous research have shown that SSL is especially effective in low labelled data regimes, we show that outperformance can be extended to high data regimes by applying Stochastic Weight Averaging (SWA), which incurs zero additional training cost. Our model was trained on a prostate CT dataset and achieved improvements of 0.12 mm, 0.14 mm, 0.32 mm, and 0.14 mm for the prostate, seminal vesicles, rectum, and bladder respectively, in terms of median test set mean surface distance (MSD) compared to the supervised baseline in our high data regime.

Automated summary

This study demonstrates the effectiveness of semi-supervised learning (SSL) in improving the performance of deep supervised models in the medical domain, even in high data regimes. By applying Stochastic Weight Averaging (SWA) technique, our model achieved significant improvements in prostate, seminal vesicles, rectum, and bladder detection compared to the supervised baseline in a prostate CT dataset.