تقرير
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation
| العنوان: | More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation |
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| المؤلفون: | Fu, Yunguan, Robu, Maria R., Koo, Bongjin, Schneider, Crispin, van Laarhoven, Stijn, Stoyanov, Danail, Davidson, Brian, Clarkson, Matthew J., Hu, Yipeng |
| سنة النشر: | 2019 |
| المجموعة: | Computer Science Statistics |
| مصطلحات موضوعية: | Electrical Engineering and Systems Science - Image and Video Processing, Computer Science - Computer Vision and Pattern Recognition, Computer Science - Machine Learning, Statistics - Machine Learning |
| الوصف: | Improving a semi-supervised image segmentation task has the option of adding more unlabelled images, labelling the unlabelled images or combining both, as neither image acquisition nor expert labelling can be considered trivial in most clinical applications. With a laparoscopic liver image segmentation application, we investigate the performance impact by altering the quantities of labelled and unlabelled training data, using a semi-supervised segmentation algorithm based on the mean teacher learning paradigm. We first report a significantly higher segmentation accuracy, compared with supervised learning. Interestingly, this comparison reveals that the training strategy adopted in the semi-supervised algorithm is also responsible for this observed improvement, in addition to the added unlabelled data. We then compare different combinations of labelled and unlabelled data set sizes for training semi-supervised segmentation networks, to provide a quantitative example of the practically useful trade-off between the two data planning strategies in this surgical guidance application. Comment: Accepted to MICCAI MIL3ID 2019 |
| نوع الوثيقة: | Working Paper |
| URL الوصول: | http://arxiv.org/abs/1908.08035 |
| رقم الأكسشن: | edsarx.1908.08035 |
| قاعدة البيانات: | arXiv |
| الوصف غير متاح. |