Abstract
Unsupervised domain adaptation (UDA) has received significant attention in medical image analysis when labels are only available for the source domain data but not for the target domain. Previous UDA methods mainly focused on the closed-set scenario, assuming that only the domain distribution shifts across domains while the label space is the same. However, in practice of medical imaging, the disease categories of training data in source domain are usually limited, and the open-world target domain data may have many unknown classes private to the source domain. Thus, open-set domain adaptation (OSDA) has great potential in this area. In this paper, we explore the OSDA problem by delving into local features for fundus disease recognition. We propose a collaborative regional clustering and alignment method to identify the common local feature patterns which are category-agnostic. Then, a cluster-aware contrastive adaptation loss is introduced to adapt the distributions based on the common local features. We also construct the first fundus image benchmark for OSDA to evaluate our methods and carry out extensive experiments for comparison. It shows that our model achieves consistent improvements over the state-of-the-art methods.
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Acknowledgement
This work was partially supported by the National Natural Science Foundation of China (Grants No 62106043), the Natural Science Foundation of Jiangsu Province (Grants No BK20210225), and the AME Programmatic Fund (A20H4b0141).
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Zhou, Y., Bai, S., Zhou, T., Zhang, Y., Fu, H. (2022). Delving into Local Features for Open-Set Domain Adaptation in Fundus Image Analysis. 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_65
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