A convolutional neural network, robust to label noise for glioma segmentation
Medical image segmentation is one of the most important tasks in radiotherapy. Recently, deep neural networks have achieved great success in solving a wide variety of medical image segmentation tasks using standardized or/and hardly preprocessed data. However, with typical clinical data, the situation drastically changes because of data complexity. For example, in radiotherapy tasks, data are much more variable in sizes and pixel intensities depending on a particular MRI machine. This heterogeneity leads to high intra- and inter-rater variability in tumor delineation. Finally, tumor contours might not coincide with pathological regions on the MRI image due to complex delineation procedures. Thus, the annotations in the existing datasets are relatively noisy. We propose a robust learning procedure based on the modern Convolutional Neural Network architecture. Our extension uses a separate module with learnable parameters, which outputs weights for the weighted cross-entropy. In our experiments, we achieved an impressive 38% boost in the Dice score using the data with high variability and target noise.