NeoUNet: Towards accurate colon polyp segmentation and neoplasm detection

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 tas...

Full description

Saved in:
Bibliographic Details
Published in:arXiv.org 2021-07
Main Authors: Phan Ngoc Lan, Nguyen Sy An, Dao Viet Hang, Dao Van Long, Tran, Quang Trung, Nguyen, Thi Thuy, Dinh, Viet Sang
Format: Article
Language:English
Subjects:
Colon
Computer architecture
Neural networks
Segmentation
Tumors
Online Access:Get full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary: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.
ISSN:2331-8422