Abstract
Optical coherence tomography (OCT) is a practical basis that is widely used for computer-aided retinal diagnosis, and OCT images from different devices show obvious intensity distribution differences. Recently, deep learning based models have achieved promising results for the classification tasks on single-device OCT images. However, when the models trained on source domain images are transferred for the classification of the target domain images from another OCT device, we can observe significant performance degradation. Re-annotating target domain images and fine-tuning models are inefficient. In this paper, we propose a multi-stage domain adaptation method (MSDA), which can learn generalized and effective domain invariant information for cross-device OCT classification from labeled source domain images and unlabeled target domain images. Specifically, task-independent feature alignment (TiFA) module firstly maps OCT images with different distributions to the same latent space, where the original image information is effectively preserved. Then, the downstream task-specific feature alignment (TsFA) module further distills out category-associated features from the output of TiFA. The experimental results demonstrate that the proposed MSDA improves the subretinal fluid classification performance of cross-device OCT images.
This study was supported in part by National Natural Science Foundation of China (62172223, 61671242), in part by Key R & D Program of Jiangsu Science and Technology Department (BE2018131) and the Fundamental Research Funds for the Central Universities (30921013105).
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Li, T., Huang, K., Zhang, Y., Li, M., Zhang, W., Chen, Q. (2022). Multi-stage Domain Adaptation for Subretinal Fluid Classification in Cross-device OCT Images. In: Wallraven, C., Liu, Q., Nagahara, H. (eds) Pattern Recognition. ACPR 2021. Lecture Notes in Computer Science, vol 13188. Springer, Cham. https://doi.org/10.1007/978-3-031-02375-0_35
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