NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection

TL;DR

NeoUNet is a novel neural network architecture designed for precise colon polyp segmentation and neoplasm detection, improving accuracy in identifying high-risk polyps during endoscopy.

Contribution

The paper introduces NeoUNet, a UNet-based model with a hybrid loss function for simultaneous polyp segmentation and neoplasm classification, advancing the state-of-the-art in this domain.

Findings

NeoUNet achieves highly competitive segmentation accuracy.

The hybrid loss function improves neoplasm classification.

Results outperform existing models on benchmark dataset.

Abstract

Automatic polyp segmentation has proven to be immensely helpful for endoscopy procedures, reducing the missing rate of adenoma detection for endoscopists while increasing efficiency. However, classifying a polyp as being neoplasm or not and segmenting it at the pixel level is still a challenging task for doctors to perform in a limited time. In this work, we propose a fine-grained formulation for the polyp segmentation problem. Our formulation aims to not only segment polyp regions, but also identify those at high risk of malignancy with high accuracy. In addition, we present a UNet-based neural network architecture called NeoUNet, along with a hybrid loss function to solve this problem. Experiments show highly competitive results for NeoUNet on our benchmark dataset compared to existing polyp segmentation models.