Abstract
An important feature of medical images is high variability, which makes it difficult to use traditional models of machine learning and tightens the requirements for the database size for training of Convolutional Neural Networks (CNN). Neural network approaches to segmentation are relevant and effective, which leads to their widespread use. However, the limited amount of training facilities available, which is typical for medical image processing, requires the search for additional solutions. In this chapter, we propose method to improve the quality of segmentation of polyps in endoscopic images using CNN introducing a preliminary stage of binary classification based on the use of global features extracted from the image. Our research show: using the binary classification as a preliminary segmentation stage increases Dice score more than 10% in conditionals of small database in CNN training.
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Obukhova, N.A., Motyko, A.A., Pozdeev, A.A. (2020). Two-Stage Method for Polyps Segmentation in Endoscopic Images. In: Favorskaya, M., Jain, L. (eds) Computer Vision in Control Systems—6. Intelligent Systems Reference Library, vol 182. Springer, Cham. https://doi.org/10.1007/978-3-030-39177-5_8
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