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4 April 2022 Transformation-consistent semi-supervised learning for prostate CT radiotherapy
Yichao Li, Mohamed S. Elmahdy, Michael S. Lew, Marius Staring
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Proceedings Volume 12033, Medical Imaging 2022: Computer-Aided Diagnosis; 120333O (2022) https://doi.org/10.1117/12.2604968
Event: SPIE Medical Imaging, 2022, San Diego, California, United States
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.
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Yichao Li, Mohamed S. Elmahdy, Michael S. Lew, and Marius Staring "Transformation-consistent semi-supervised learning for prostate CT radiotherapy", Proc. SPIE 12033, Medical Imaging 2022: Computer-Aided Diagnosis, 120333O (4 April 2022); https://doi.org/10.1117/12.2604968
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KEYWORDS
Prostate
Image segmentation
Bladder
Rectum
Computed tomography
Radiotherapy
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