  This paper presents an automatic algorithm for the segmentation of areas
affected by an acute stroke on the non-contrast computed tomography brain
images. The proposed algorithm is designed for learning in a weakly supervised
scenario when some images are labeled accurately, and some images are labeled
inaccurately. Wrong labels appear as a result of inaccuracy made by a
radiologist in the process of manual annotation of computed tomography images.
We propose methods for solving the segmentation problem in the case of
inaccurately labeled training data. We use the U-Net neural network
architecture with several modifications. Experiments on real computed
tomography scans show that the proposed methods increase the segmentation
accuracy.
