Dysplasia grading of colorectal polyps through CNN analysis of WSI
This addresses the need for automated histopathological analysis to assist pathologists in colorectal cancer risk assessment, but it is incremental as it applies existing deep learning methods to a specific medical imaging task.
The paper tackled the problem of grading dysplasia in colorectal polyps from whole slide images using a convolutional neural network, achieving 70% accuracy, which matches pathologists' concordance.
Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histopathological characterization of colorectal polyps is the major instrument for the pathologist in order to infer the actual risk for cancer and to guide further follow-up. Colorectal polyps diagnosis includes the evaluation of the polyp type, and more importantly, the grade of dysplasia. This latter evaluation represents a critical step for the clinical follow-up. The proposed deep learning-based classification pipeline is based on state-of-the-art convolutional neural network, trained using proper countermeasures to tackle WSI high resolution and very imbalanced dataset. The experimental results show that one can successfully classify adenomas dysplasia grade with 70% accuracy, which is in line with the pathologists' concordance.