Fu, Y;
Robu, MR;
Koo, B;
Schneider, C;
Laarhoven, SV;
Stoyanov, D;
Davidson, B;
... Hu, Y; + view all
More unlabelled data or label more data? A study on semi-supervised laparoscopic image segmentation.
In: Wang, Q.et al., (ed.)
Domain Adaptation and Representation Transfer and Medical Image Learning with Less Labels and Imperfect Data. DART 2019, MIL3ID 2019.
(pp. pp. 173-180).
Springer: Cham.
Preview |
Text
1908.08035v1.pdf - Accepted Version Download (1MB) | Preview |
Abstract
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.
Archive Staff Only
 |
View Item |
