Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke

• URL: http://arxiv.org/abs/2109.01887v1
• Date: Sat, 4 Sep 2021 15:24:38 GMT
• Title: Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke
• Authors: Anna Dobshik, Andrey Tulupov, Vladimir Berikov
• Abstract summary: 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.
• Score: 0.0
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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