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 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.

Dysplasia Grading of Colorectal Polyps Through Convolutional Neural Network Analysis of Whole Slide Images

Perlo D.;Tartaglione E.;Bertero L.;Cassoni P.;Grangetto M.
2021

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 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.
International Conference on Medical Imaging and Computer-Aided Diagnosis, MICAD 2021
virtual
2021
Lecture Notes in Electrical Engineering
Springer Science and Business Media Deutschland GmbH
784
325
334
978-981-16-3879-4
978-981-16-3880-0
Colorectal adenomas; Colorectal polyps; Deep learning; Digital pathology; Multi resolution
Perlo D.; Tartaglione E.; Bertero L.; Cassoni P.; Grangetto M.
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Utilizza questo identificativo per citare o creare un link a questo documento: http://hdl.handle.net/2318/1844234
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