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An Interpretable Transformer Network for the Retinal Disease Classification Using Optical Coherence Tomography by Jingzhen He; Junxia Wang; Zeyu Han; Jun Ma; Chongjing Wang; Meng Qi is a Biochemistry, Genetics and Molecular Biology article available to read on EtoBox.

What is An Interpretable Transformer Network for the Retinal Disease Classification Using Optical Coherence Tomography about?

## Abstract Retinal illnesses such as age-related macular degeneration and diabetic macular edema will lead to irreversible blindness. With optical coherence tomography (OCT), doctors are able to see cross-sections of the retinal layers and provide patients with a diagnosis. Manual reading of OCT images is time-consuming, labor-intensive and even error-prone. Computer-aided diagnosis algorithms improve efficiency by automatically analyzing and diagnosing retinal OCT images. However, the accuracy and interpretability of these algorithms can be further improved through effective feature extraction, loss optimization and visualization analysis. In this paper, we propose an interpretable Swin-Poly Transformer network for performing automatically retinal OCT image classification. By shifting the window partition, the Swin-Poly Transformer constructs connections between neighboring non-overlapping windows in the previous layer and thus has the flexibility to model multi-scale features. Besides, the Swin-Poly Transformer modifies the importance of polynomial bases to refine cross entropy for better retinal OCT image classification. In addition, the proposed method also provides confidence

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Author

Jingzhen He; Junxia Wang; Zeyu Han; Jun Ma; Chongjing Wang; Meng Qi

Publisher

Springer Science and Business Media LLC

Published

2023

Language

EN

Field

Biochemistry, Genetics and Molecular Biology (Life Sciences)