Paper Info
Title
Domain Generalization for Mammography Detection via Multi-style and Multi-view Contrastive Learning
Abstract
Lesion detection is a fundamental problem in the computer-aided diagnosis scheme for mammography. The advance of deep learning techniques have made a remarkable progress for this task, provided that the training data are large and sufficiently diverse in terms of image style and quality. In particular, the diversity of image style may be majorly attributed to the vendor factor. However, the collection of mammograms from vendors as many as possible is very expensive and sometimes impractical for laboratory-scale studies. Accordingly, to further augment the generalization capability of deep learning model to various vendors with limited resources, a new contrastive learning scheme is developed. Specifically, the backbone network is firstly trained with a multi-style and multi-view unsupervised self-learning scheme for the embedding of invariant features to various vendor-styles. Afterward, the backbone network is then recalibrated to the downstream task of lesion detection with the specific supervised learning. The proposed method is evaluated with mammograms from four vendors and one unseen public dataset. The experimental results suggest that our approach can effectively improve detection performance on both seen and unseen domains, and outperforms many state-of-the-art (SOTA) generalization methods.
Year
DOI
Venue
2021
10.1007/978-3-030-87234-2_10
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT VII
Keywords
DocType
Volume
Domain generalization, Breast lesion detection, Contrastive learning
Conference
12907
ISSN
Citations 
PageRank 
0302-9743
0
0.34
References 
Authors
0
10
Name
Order
Citations
PageRank
Zheren Li100.68
Zhiming Cui246.48
Sheng Wang301.35
Yuji Qi400.34
Xi Ouyang5274.86
Qitian Chen600.68
Yuezhi Yang700.34
Zhong Xue849645.70
Dinggang Shen97837611.27
Jie-Zhi Cheng1010213.00