Deep image mining for diabetic retinopathy screening.
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TLDR
In this article, a generalization of the backpropagation method is proposed in order to train ConvNets that produce high-quality heatmaps, showing which pixels in images play a role in the image-level predictions.About:
This article is published in Medical Image Analysis.The article was published on 2017-07-01 and is currently open access. It has received 346 citations till now.read more
Citations
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Journal ArticleDOI
Artificial intelligence and deep learning in ophthalmology
Daniel Shu Wei Ting,Louis R. Pasquale,Lily Peng,John P. Campbell,Aaron Y. Lee,Rajiv Raman,Gavin Tan,Leopold Schmetterer,Pearse A. Keane,Tien Yin Wong +9 more
TL;DR: There are also potential challenges with DL application in ophthalmology, including clinical and technical challenges, explainability of the algorithm results, medicolegal issues, and physician and patient acceptance of the AI ‘black-box’ algorithms.
Journal ArticleDOI
Automated Grading of Age-Related Macular Degeneration From Color Fundus Images Using Deep Convolutional Neural Networks
Philippe Burlina,Neil Joshi,Michael Pekala,Katia D. Pacheco,David E. Freund,Neil M. Bressler +5 more
TL;DR: It is demonstrated that automated algorithms could play a role that is independent of expert human graders in the current management of AMD and could address the costs of screening or monitoring, access to health care, and the assessment of novel treatments that address the development or progression of AMD.
Journal ArticleDOI
Fundus Image Classification Using VGG-19 Architecture with PCA and SVD
TL;DR: The proposed VGG-19 DNN based DR model outperformed the AlexNet and spatial invariant feature transform (SIFT) in terms of classification accuracy and computational time.
Journal ArticleDOI
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek,Grégoire Montavon,Sebastian Lapuschkin,Christopher J. Anders,Klaus-Robert Müller +4 more
TL;DR: In this paper, the authors provide a timely overview of explainable AI, with a focus on 'post-hoc' explanations, explain its theoretical foundations, and put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations.
Journal ArticleDOI
Explaining Deep Neural Networks and Beyond: A Review of Methods and Applications
Wojciech Samek,Grégoire Montavon,Sebastian Lapuschkin,Christopher J. Anders,Klaus-Robert Müller +4 more
TL;DR: In this paper, the authors provide a timely overview of post hoc explanations and explain its theoretical foundations, and put interpretability algorithms to a test both from a theory and comparative evaluation perspective using extensive simulations, and demonstrate successful usage of XAI in a representative selection of application scenarios.
References
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