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"Transformation-Consistent Semi-Supervised Learning for Prostate CT Radiotherapy"
Yichao Li, Mohamed S. Elmahdy, Michael S. Lew and Marius Staring
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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Copyright © 2022 by the authors.
Published version © 2022 by SPIE.
Personal use of this material is permitted. However, permission to reprint or republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works, must be obtained from the copyright holder.
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BibTeX entry
| @inproceedings{Li:2022, |
| author |
= {Li, Yichao and Elmahdy, Mohamed S. and Lew, Michael S. and Staring, Marius}, |
| title |
= {Transformation-Consistent Semi-Supervised Learning for Prostate CT Radiotherapy}, |
| booktitle |
= {SPIE Medical Imaging: Computer-Aided Diagnosis}, |
| editor |
= {Colliot, Olivier and Išgum, Ivana}, |
| address |
= {San Diego, CA, USA}, |
| series |
= {Proceedings of SPIE}, |
| volume |
= {12033}, |
| pages |
= {120333O}, |
| month |
= {February}, |
| year |
= {2022}, |
| } |
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| last modified: 11-04-2022 |
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