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Unsupervised domain adaptation with adversarial learning for mass detection in mammogram
- Source :
- Neurocomputing. 393:27-37
- Publication Year :
- 2020
- Publisher :
- Elsevier BV, 2020.
-
Abstract
- Many medical image datasets have been collected without proper annotations for deep learning training. In this paper, we propose a novel unsupervised domain adaptation framework with adversarial learning to minimize the annotation efforts. Our framework employs a task specific network, i.e., fully convolutional network (FCN), for spatial density prediction. Moreover, we employ a domain discriminator, in which adversarial learning is adopted to align the less-annotated target domain features with the well-annotated source domain features in the feature space. We further propose a novel training strategy for the adversarial learning by coupling data from source and target domains and alternating the subnet updates. We employ the public CBIS-DDSM dataset as the source domain, and perform two sets of experiments on two target domains (i.e., the public INbreast dataset and a self-collected dataset), respectively. Experimental results suggest consistent and comparable performance improvement over the state-of-the-art methods. Our proposed training strategy is also proved to converge much faster.
- Subjects :
- 0209 industrial biotechnology
Computer science
business.industry
Cognitive Neuroscience
Feature vector
Deep learning
02 engineering and technology
Machine learning
computer.software_genre
Computer Science Applications
Image (mathematics)
Domain (software engineering)
020901 industrial engineering & automation
Artificial Intelligence
0202 electrical engineering, electronic engineering, information engineering
020201 artificial intelligence & image processing
Artificial intelligence
business
computer
Subjects
Details
- ISSN :
- 09252312
- Volume :
- 393
- Database :
- OpenAIRE
- Journal :
- Neurocomputing
- Accession number :
- edsair.doi...........bfe9c91f397a6adfdb6412e776620c11