research-article

ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing

Authors:

Cheng ZhuoAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 18, Issue 2

Article No.: 27, Pages 1 - 17

https://doi.org/10.1145/3451213

Published: 31 December 2021 Publication History

Abstract

Pathology image segmentation is an essential step in early detection and diagnosis for various diseases. Due to its complex nature, precise segmentation is not a trivial task. Recently, deep learning has been proved as an effective option for pathology image processing. However, its efficiency is highly restricted by inconsistent annotation quality. In this article, we propose an accurate and noise-tolerant segmentation approach to overcome the aforementioned issues. This approach consists of two main parts: a preprocessing module for data augmentation and a new neural network architecture, ANT-UNet. Experimental results demonstrate that, even on a noisy dataset, the proposed approach can achieve more accurate segmentation with 6% to 35% accuracy improvement versus other commonly used segmentation methods. In addition, the proposed architecture is hardware friendly, which can reduce the amount of parameters to one-tenth of the original and achieve 1.7× speed-up.

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Cited By

Zhang XGleber-Netto FWang SJin KYang DGillenwater AMyers JFerrarotto RPickering CXiao G(2023)A Deep Learning Onion Peeling Approach to Measure Oral Epithelium Layer NumberCancers10.3390/cancers1515389115:15(3891)Online publication date: 31-Jul-2023
https://doi.org/10.3390/cancers15153891

Index Terms

ANT-UNet: Accurate and Noise-Tolerant Segmentation for Pathology Image Processing
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Artificial intelligence
    1. Computer vision
      1. Computer vision problems
        Image segmentation

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Information

Published In

cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 18, Issue 2

April 2022

411 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/3508462

Editor:
Ramesh Karri
Polytechnic Institute of New York University, USA

Issue’s Table of Contents

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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 31 December 2021

Accepted: 01 February 2021

Revised: 01 December 2020

Received: 01 August 2020

Published in JETC Volume 18, Issue 2

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Zhejiang Provincial Innovation Team
National Natural Science Foundation of China

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Cited By

Zhang XGleber-Netto FWang SJin KYang DGillenwater AMyers JFerrarotto RPickering CXiao G(2023)A Deep Learning Onion Peeling Approach to Measure Oral Epithelium Layer NumberCancers10.3390/cancers1515389115:15(3891)Online publication date: 31-Jul-2023
https://doi.org/10.3390/cancers15153891

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