Submit

Login Login

Register Register

Home / Journals / CMC / Online First / doi:10.32604/cmc.2024.056478
Journal Menu
Special Issues
Table of Content

All issues

Online First

2024

2023

2022

2021

2020

2019

2018

2017

2016

2015

2014

2013

2012

2011

2010

2009

2008

2007

2006

2005

2004

Open Access

ARTICLE

Stochastic Augmented-Based Dual-Teaching for Semi-Supervised Medical Image Segmentation

Hengyang Liu1, Yang Yuan1,*, Pengcheng Ren1, Chengyun Song1, Fen Luo2
1 School of Computer Science and Engineering, Chongqing University of Technology, Chongqing, 400054, China
2 College of Artificial Intelligence, Chongqing Technology and Business University, Chongqing, 400067, China
* Corresponding Author: Yang Yuan. Email: email
(This article belongs to the Special Issue: Deep Learning in Computer-Aided Diagnosis Based on Medical Image)

Computers, Materials & Continua https://doi.org/10.32604/cmc.2024.056478

Received 23 July 2024; Accepted 06 October 2024; Published online 13 November 2024

Abstract

Existing semi-supervised medical image segmentation algorithms use copy-paste data augmentation to correct the labeled-unlabeled data distribution mismatch. However, current copy-paste methods have three limitations: (1) training the model solely with copy-paste mixed pictures from labeled and unlabeled input loses a lot of labeled information; (2) low-quality pseudo-labels can cause confirmation bias in pseudo-supervised learning on unlabeled data; (3) the segmentation performance in low-contrast and local regions is less than optimal. We design a Stochastic Augmentation-Based Dual-Teaching Auxiliary Training Strategy (SADT), which enhances feature diversity and learns high-quality features to overcome these problems. To be more precise, SADT trains the Student Network by using pseudo-label-based training from Teacher Network 1 and supervised learning with labeled data, which prevents the loss of rare labeled data. We introduce a bi-directional copy-paste mask with progressive high-entropy filtering to reduce data distribution disparities and mitigate confirmation bias in pseudo-supervision. For the mixed images, Deep-Shallow Spatial Contrastive Learning (DSSCL) is proposed in the feature spaces of Teacher Network 2 and the Student Network to improve the segmentation capabilities in low-contrast and local areas. In this procedure, the features retrieved by the Student Network are subjected to a random feature perturbation technique. On two openly available datasets, extensive trials show that our proposed SADT performs much better than the state-of-the-art semi-supervised medical segmentation techniques. Using only 10% of the labeled data for training, SADT was able to acquire a Dice score of 90.10% on the ACDC (Automatic Cardiac Diagnosis Challenge) dataset.

Keywords

Semi-supervised; medical image segmentation; contrastive learning; stochastic augmented

  • 78

    View

  • 10

    Download

  • 0

    Like

Related articles
Copyright© 2024 Tech Science Press
© 1997-2024 TSP (Henderson, USA) unless otherwise stated

Share Link