期刊
MEDICAL IMAGE COMPUTING AND COMPUTER ASSISTED INTERVENTION - MICCAI 2021, PT I
卷 12901, 期 -, 页码 451-460出版社
SPRINGER INTERNATIONAL PUBLISHING AG
DOI: 10.1007/978-3-030-87193-2_43
关键词
Image segmentation; Style transfer; Curriculum learning
类别
资金
- National Key R&D Program of China [2019YFC0118300]
- Shenzhen Peacock Plan [KQTD2016053112051497, KQJSCX20180328095606003]
- Royal Academy of Engineering under the RAEng Chair in Emerging Technologies scheme [CiET1919/19]
- EPSRC TUSCA [EP/V04799X/1]
- Royal Society CROSSLINK Exchange Programme [IES/NSFC/201380]
- European Union's Horizon 2020 research and innovation program [825903]
- Spanish Ministry of Science, Innovation and Universities [RTI2018-099898-B-I00]
This paper proposes a new framework to address the issue of distribution shifts in image intensities between training and test data sets in deep segmentation models. The framework includes a novel curriculum design inspired by curriculum learning, an automated gradient manipulation method, and a Local Gradient Sign strategy. Extensive experiments on the public M&Ms Challenge dataset show that the proposed framework can generalize deep models well to unknown distributions and achieve significant improvements in segmentation accuracy.
The performance of deep segmentation models often degrades due to distribution shifts in image intensities between the training and test data sets. This is particularly pronounced in multi-centre studies involving data acquired using multi-vendor scanners, with variations in acquisition protocols. It is challenging to address this degradation because the shift is often not known a priori and hence difficult to model. We propose a novel framework to ensure robust segmentation in the presence of such distribution shifts. Our contribution is three-fold. First, inspired by the spirit of curriculum learning, we design a novel style curriculum to train the segmentation models using an easy-to-hard mode. A style transfer model with style fusion is employed to generate the curriculum samples. Gradually focusing on complex and adversarial style samples can significantly boost the robustness of the models. Second, instead of subjectively defining the curriculum complexity, we adopt an automated gradient manipulation method to control the hard and adversarial sample generation process. Third, we propose the Local Gradient Sign strategy to aggregate the gradient locally and stabilise training during gradient manipulation. The proposed framework can generalise to unknown distribution without using any target data. Extensive experiments on the public M&Ms Challenge dataset demonstrate that our proposed framework can generalise deep models well to unknown distributions and achieve significant improvements in segmentation accuracy.
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