IGU-Aug: Information-Guided Unsupervised Augmentation and Pixel-Wise Contrastive Learning for Medical Image Analysis

doi:10.1109/TMI.2024.3436713

	IGU-Aug: Information-Guided Unsupervised Augmentation and Pixel-Wise Contrastive Learning for Medical Image Analysis
	Quan, Quan 1,2; Yao, Qingsong 1,2; Zhu, Heqin 3,4; Zhou, S. Kevin 1,5
	2025
发表期刊	IEEE TRANSACTIONS ON MEDICAL IMAGING
ISSN	0278-0062
卷号	44 期号:1 页码:154-164
摘要	Contrastive learning (CL) is a form of self-supervised learning and has been widely used for various tasks. Different from widely studied instance-level contrastive learning, pixel-wise contrastive learning mainly helps with pixel-wise dense prediction tasks. The counterpart to an instance in instance-level CL is a pixel, along with its neighboring context, in pixel-wise CL. Aiming to build better feature representation, there is a vast literature about designing instance augmentation strategies for instance-level CL; but there is little similar work on pixel augmentation for pixel-wise CL with a pixel granularity. In this paper, we attempt to bridge this gap. We first classify a pixel into three categories, namely low-, medium-, and high-informative, based on the information quantity the pixel contains. We then adaptively design separate augmentation strategies for each category in terms of augmentation intensity and sampling ratio. Extensive experiments validate that our information-guided pixel augmentation strategy succeeds in encoding more discriminative representations and surpassing other competitive approaches in unsupervised local feature matching. Furthermore, our pretrained model improves the performance of both one-shot and fully supervised models. To the best of our knowledge, we are the first to propose a pixel augmentation method with a pixel granularity for enhancing unsupervised pixel-wise contrastive learning. Code is available at https://github.com/Curli-quan/IGU-Aug.
关键词	Contrastive learning Task analysis Data augmentation Semantics Training Feature extraction Image segmentation data augmentation few-shot learning landmark detection segmentation
DOI	10.1109/TMI.2024.3436713
收录类别	SCI
语种	英语
资助项目	Natural Science Foundation of China[62271465] ; Suzhou Basic Research Program[SYG202338] ; Open Fund Project of Guangdong Academy of Medical Sciences, China[YKY-KF202206]
WOS研究方向	Computer Science ; Engineering ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS类目	Computer Science, Interdisciplinary Applications ; Engineering, Biomedical ; Engineering, Electrical & Electronic ; Imaging Science & Photographic Technology ; Radiology, Nuclear Medicine & Medical Imaging
WOS记录号	WOS:001389746700019
出版者	IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
引用统计
文献类型	期刊论文
条目标识符	http://119.78.100.204/handle/2XEOYT63/40785
专题	中国科学院计算技术研究所期刊论文_英文
通讯作者	Zhou, S. Kevin
作者单位	1.Chinese Acad Sci, Inst Comp Technol, Beijing 100045, Peoples R China 2.Univ Chinese Acad Sci, Inst Comp Technol, Beijing 101408, Peoples R China 3.Univ Sci & Technol China, Sch Biomed Engn, Hefei 230026, Peoples R China 4.Univ Sci & Technol China, Suzhou Inst Adv Res, Hefei 230026, Peoples R China 5.Univ Sci & Technol China, Suzhou Inst Adv Res, Sch Biomed Engn, Hefei 230026, Peoples R China
推荐引用方式 GB/T 7714	Quan, Quan,Yao, Qingsong,Zhu, Heqin,et al. IGU-Aug: Information-Guided Unsupervised Augmentation and Pixel-Wise Contrastive Learning for Medical Image Analysis[J]. IEEE TRANSACTIONS ON MEDICAL IMAGING,2025,44(1):154-164.
APA	Quan, Quan,Yao, Qingsong,Zhu, Heqin,&Zhou, S. Kevin.(2025).IGU-Aug: Information-Guided Unsupervised Augmentation and Pixel-Wise Contrastive Learning for Medical Image Analysis.IEEE TRANSACTIONS ON MEDICAL IMAGING,44(1),154-164.
MLA	Quan, Quan,et al."IGU-Aug: Information-Guided Unsupervised Augmentation and Pixel-Wise Contrastive Learning for Medical Image Analysis".IEEE TRANSACTIONS ON MEDICAL IMAGING 44.1(2025):154-164.