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Object of study is the segmentation of cardiac ventricles on MRI images using deep learning architectures. The aim is to evaluate and compare the effectiveness of various deep learning architectures for the segmentation of MRI images of ventricles, to improve the quality and speed of medical data processing. As a result of the research, a comparison of
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The paper considers the problem of reducing the number of correct predictions when computer vision systems process adversarial images. The main purpose is to find a way to solve the problem by developing a method for dealing with adversarial noise in images by neural networks as powerful and universal tools for working with such data.
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