Comparative Analysis of Deep Neural Network and Texture-Based Classifiers for Recognition of Acute Stroke using Non-Contrast CT Images

Nedel’ko, V. M.; Kozinets, Roman; Tulupov, Andrey; Berikov, Vladimir

doi:10.1109/usbereit48449.2020.9117784

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Cited by 6 publications

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“…It is worth noting that in the problems of semantic segmentation of medical images fully convolutional neural networks (CNNs) show a better performance in comparison with classical machine learning methods such as, for example, kNN, SVM, Random Forest, and Adaboost classifiers [2].…”

Section: Related Workmentioning

confidence: 99%

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

Dobshik,

Tulupov,

Berikov

2021

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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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Section: Related Workmentioning

confidence: 99%