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

Source: [Weakly supervised semantic segmentation of tomographic images in the diagnosis of stroke](<https://www.sota2.com/research/paper/68fa6b801e209131cea2ea79>)

Collection index: [llms.txt](<https://www.sota2.com/research/llms.txt>)

Data updated: 2025-10-25T23:50:06.694Z

Authors: Anna Dobshik, Andrey Tulupov, Vladimir Berikov

Published: 2021-09-04T15:24:38.000Z

## 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.

## Recorded benchmark results

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## Original sources and implementation

[Original paper](<https://arxiv.org/abs/2109.01887>)

[PDF](<https://arxiv.org/pdf/2109.01887.pdf>)