Features of Intelligent Processing of Cytological Whole Slide Images
Abstract
The purpose of this work is to study the features of intelligent processing of full-slide cytological images using the example of working with cytological images of a thyroid puncture (thyroid gland) measuring 3-5 GB each. To do this, based on the analysis of scientific publications and the described approaches to the intelligent analysis of cytological images, specific actions for processing large images were identified. An approach using computer vision has been developed for multi-class categorization of images of thyroid cytological slides using the international Bethesda system and the selection of informative features that influence the categorization process, as well as training and testing of models. The target metrics for comparing the effectiveness of models were chosen for segmentation: Intersection over Union, Dice coefficient; for classification: accuracy, precision, recall, f1-score. The result of the work was the practical implementation of an approach to processing and intelligent analysis of cytological images of the thyroid gland using computer vision.
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