Identifying regions of interest in whole slide images of renal cell carcinoma
Résumé
Purpose: The histopathological images contain a huge amount of information, which can make diagnosis an extremely time-consuming and tedioustask. In this study, we developed a completely automated system to detect regions-of-interest (ROIs) in Whole Slide Images (WSI) of Renal Cell Carcinoma (RCC), to reduce time analysis and assist pathologists in making more accurate decisions.Methods: For this purpose, the WSIs are divided into patches at high resolution and a method is proposed to classify the patches into a tumor andhealthy tissue. The proposed approach is based on an e cient texture descriptor named Dominant Rotated Local Binary Pattern (DRLBP) and colortransformation (H and V channels) to reveal and exploit the immense texture variability at the microscopic high magni cations level. Thereby, the DRLBPs retain the structural information and utilize the magnitude values in a local neighborhood for more discriminative power. For the classication of the relevant ROIs, feature extraction of WSIs patches was performed on the color channels separately to form the histograms. Next, we used the most frequently occurring patterns as a feature selection step to discard non-informative features. The performances of di erent classi ers (k-NN, SVM and RF) on a set of 1800 kidney cancer patches originating from 12 whole slide images, were compared and evaluated. Furthermore, the small size of the image dataset allows to investigate deep learning approach based on transfer learning for image patches classi cation by using deep features (VGG-16) and ne-tuning (ResNet-50) methods.Results: High recognition accuracy was obtained and the classi ers are efficient, the best precision result was 99.17% achieved with SVM. Moreover, transfer learning models perform well with comparable performance, and the highest precision using ResNet-50 reached 98.50%. The proposed approach results revealed a very e cient image classi cation and demonstrated e cacy in identifying ROIs.Conclusion: This study presents an automatic system to detect regions of interest relevant to the diagnosis of kidney cancer in whole-slide histopathology images.
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