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Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images

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Medical Image Computing and Computer Assisted Intervention – MICCAI 2022 (MICCAI 2022)

Abstract

In medical image classification tasks like the detection of diabetic retinopathy from retinal fundus images, it is highly desirable to get visual explanations for the decisions of black-box deep neural networks (DNNs). However, gradient-based saliency methods often fail to highlight the diseased image regions reliably. On the other hand, adversarially robust models have more interpretable gradients than plain models but suffer typically from a significant drop in accuracy, which is unacceptable for clinical practice. Here, we show that one can get the best of both worlds by ensembling a plain and an adversarially robust model: maintaining high accuracy but having improved visual explanations. Also, our ensemble produces meaningful visual counterfactuals which are complementary to existing saliency-based techniques. Code is available under https://github.com/valentyn1boreiko/Fundus_VCEs.

V. Boreiko and I. Ilanchezian–Equal Contribution.

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Notes

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Acknowledgement

We acknowledge support by the German Ministry of Science and Education (BMBF, 01GQ1601 and 01IS18039A) and the German Science Foundation (BE5601/8-1 and EXC 2064, project number 390727645). The authors thank the International Max Planck Research School for Intelligent Systems (IMPRS-IS) for supporting I.I.

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Correspondence to Valentyn Boreiko .

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Boreiko, V. et al. (2022). Visual Explanations for the Detection of Diabetic Retinopathy from Retinal Fundus Images. 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 13432. Springer, Cham. https://doi.org/10.1007/978-3-031-16434-7_52

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  • DOI: https://doi.org/10.1007/978-3-031-16434-7_52

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