Transformation-consistent semi-supervised learning for prostate CT radiotherapy

SPIE Medical Imaging: Computer-Aided Diagnosis, pp. 76

Abstract

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.

Authors 4

1. Yichao Li Aachen Leiden Univ. (Netherlands)

   Leiden University

   Affiliation as printed

   Leiden Univ. (Netherlands)

2. Mohamed S. Elmahdy Aachen Leiden Univ. Medical Ctr

   Leiden University Medical Center

   Affiliation as printed

   Leiden Univ. Medical Ctr. (Netherlands)

3. Michael S. Lew Aachen Leiden Univ. (Netherlands)

   Leiden University

   Affiliation as printed

   Leiden Univ. (Netherlands)

4. Marius Staring Aachen Leiden Univ. Medical Ctr

   Leiden University Medical Center

   Affiliation as printed

   Leiden Univ. Medical Ctr. (Netherlands)