# Dysplasia grading of colorectal polyps through CNN analysis of WSI

arXiv: https://arxiv.org/abs/2102.05498 · HTML: https://mlsanity.com/paper/2102.05498/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 6 of its 26 references that are in the map.
- First posted on arXiv 10 Feb 2021.

## Builds on (most cited first, 6 of 6)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Grad-CAM: Visual Explanations from Deep Networks via Gradient-based Localization](https://mlsanity.com/paper/1610.02391/) (2016)
- [B-CNN: Branch Convolutional Neural Network for Hierarchical Classification](https://mlsanity.com/paper/1709.09890/) (2017)
- [UniToPatho, a labeled histopathological dataset for colorectal polyps classification and adenoma dysplasia grading](https://mlsanity.com/paper/2101.09991/) (2021)
- [Deep-Learning for Classification of Colorectal Polyps on Whole-Slide Images](https://mlsanity.com/paper/1703.01550/) (2017)
- [Structure Preserving Stain Normalization of Histopathology Images Using Self-Supervised Semantic Guidance](https://mlsanity.com/paper/2008.02101/) (2020)

## Built on by (most cited first, 1 of 1)

- [XtraLight-MedMamba for Classification of Neoplastic Tubular Adenomas](https://mlsanity.com/paper/2602.04819/) (2026)

## Abstract (arXiv)

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.