| Autor: |
Peng, Cao, Qingshan, Hou, Ruoxian, Song, Haonan, Wang, Osmar, Zaiane |
| Rok vydání: |
2022 |
| Předmět: |
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| Zdroj: |
Computers in Biology and Medicine. 144:105341 |
| ISSN: |
0010-4825 |
| DOI: |
10.1016/j.compbiomed.2022.105341 |
| Popis: |
Early detection and treatment of diabetic retinopathy (DR) can significantly reduce the risk of vision loss in patients. In essence, we are faced with two challenges: (i) how to simultaneously achieve domain adaptation from the different domains and (ii) how to build an interpretable multi-instance learning (MIL) on the target domain in an end-to-end framework. In this paper, we address these issues and propose a unified weakly-supervised domain adaptation framework, which consists of three components: domain adaptation, instance progressive discriminator and multi-instance learning with attention. The method models the relationship between the patches and images in the target domain with a multi-instance learning scheme and an attention mechanism. Meanwhile, it incorporates all available information from both source and target domains for a jointly learning strategy. We validate the performance of the proposed framework for DR grading on the Messidor dataset and the large-scale Eyepacs dataset. The experimental results demonstrate that it achieves an average accuracy of 0.949 (95% CI 0.931-0.958)/0.764 (95% CI 0.755-0.772) and an average AUC value of 0.958 (95% CI 0.945-0.962)/0.749 (95% CI 0.732-0.761) for binary-class/multi-class classification tasks on the Messidor dataset. Moreover, the proposed method achieves an accuracy of 0.887 and a quadratic weighted kappa score value of 0.860 on the Eyepacs dataset, outperforming the state-of-the-art approaches. Comprehensive experiments confirm the effectiveness of the approach in terms of both grading performance and interpretability. The source code is available at https://github.com/HouQingshan/WAD-Net. |
| Databáze: |
OpenAIRE |
| Externí odkaz: |
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