Dysplasia grading of colorectal polyps through CNN analysis of WSI

Abstract

Colorectal cancer is a leading cause of cancer death for both men and women. For this reason, histo-pathological 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 learningbased 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.

Classification of Colorectal Polyps from Histopathological Images using Ensemble of ConvNeXt Variants

“…Performance of the proposed methodology is evaluated by using 238 external slides. Perlo et al [18] introduce a methodology to grade the dysplasia of the colorectal polyps. Moreover, in their work they compare the model performance on gray scale, RGB and Macenko stain normalized histology images.…”

“…Moreover, DL based approaches are actively employed for pattern classification or analysis of the medical images. There are extensive studies conducted with numerous methods focusing on the individual diagnosis of colorectal cancer from histopathology images, such as classification of colorectal adenocarcinoma [ [2], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15]], colon polyp classification [ [16], [17], [4], [18], [19], [20], [21]] and colon gland classification [ [5], [22], [23]].…”