Abstract
To overcome the barriers of multimodality and scarcity of annotations in medical image segmentation, many unsupervised domain adaptation (UDA) methods have been proposed, especially in cardiac segmentation. However, these methods may not completely avoid the interference of domain-specific information. To tackle this problem, we propose a novel Attention-enhanced Disentangled Representation (ADR) learning model for UDA in cardiac segmentation. To sufficiently remove domain shift and mine more precise domain-invariant features, we first put forward a strategy from image-level coarse alignment to fine removal of remaining domain shift. Unlike previous dual path disentanglement methods, we present channel-wise disentangled representation learning to promote mutual guidance between domain-invariant and domain-specific features. Meanwhile, Hilbert-Schmidt independence criterion (HSIC) is adopted to establish the independence between the disentangled features. Furthermore, we propose an attention bias for adversarial learning in the output space to enhance the learning of task-relevant domain-invariant features. To obtain more accurate predictions during inference, an information fusion calibration (IFC) is also proposed. Extensive experiments on the MMWHS 2017 dataset demonstrate the superiority of our method. Code is available at https://github.com/Sunxy11/ADR.
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Notes
- 1.
In fact, we simply make fine-tuning on the pre-trained weights without introducing additional complex reconstruction loss, thus facilitating the stable training of the overall model.
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Acknowledgement
This work was supported in part by the Science and Technology Innovation 2030 - New Generation Artificial Intelligence Major Project under Grant No. 2018AAA0102100, Beijing Natural Science Foundation under Grant No. 7222313, and National Natural Science Foundation of China under Grant No. 61976018.
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Sun, X., Liu, Z., Zheng, S., Lin, C., Zhu, Z., Zhao, Y. (2022). Attention-Enhanced Disentangled Representation Learning for Unsupervised Domain Adaptation in Cardiac Segmentation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13437. Springer, Cham. https://doi.org/10.1007/978-3-031-16449-1_71
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