Weakly-supervised localization of diabetic retinopathy lesions in retinal fundus images

By Waleed M. Gondal, Jan M. Köhler, René Grzeszick, Gernot A. Fink, Michael Hirsch. 2017

152 citations recorded by the source corpus.

Convolutional neural networks (CNNs) show impressive performance for image classification and detection, extending heavily to the medical image domain. Nevertheless, medical experts are skeptical in these predictions as the nonlinear multilayer structure resulting in a classification outcome is not directly graspable. Recently, approaches have been shown which help the user to understand the discriminative regions within an image which are decisive for the CNN to conclude to a certain class. Although these approaches could help to build trust in the CNNs predictions, they are only slightly shown to work with medical image data which often poses a challenge as the decision for a class relies on different lesion areas scattered around the entire image. Using the DiaretDB1 dataset, we show that on retina images different lesion areas fundamental for diabetic retinopathy are detected on an image level with high accuracy, comparable or exceeding supervised methods. On lesion level, we achieve few false positives with high sensitivity, though, the network is solely trained on image-level labels which do not include information about existing lesions. Classifying between diseased and healthy images, we achieve an AUC of 0.954 on the DiaretDB1.

Subfields: Artificial Intelligence, Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging

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Citations in the corpus

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks (2019, methodology): “[18] use a comparable setting to ours applying CAM [53] in a weakly-supervised way to highlight important regions.”
Visual Feature Attribution Using Wasserstein GANs (2017, background): “Pinpointing all evidence of a class is important for a variety of tasks such as weakly supervised localisation or segmentation of structures [41, 43, 65], and better understanding disease effects, and physiological or pathological processes in medical images [67, 18, 12, 19, 13, 27, 54, 30, 31, 63].”
Applications of Deep Learning in Fundus Images: A Review (2021, methodology): “Gondal et al. (2017) adopted unsupervised learning to perform DR grading and generate lesion heatmaps using only image-level labels. Their main network uses the o O solution (Mathis Antony, 2015), replacing the last dense layer with a global average pooling (GAP) layer. Their way of generating heatm”
Weakly Supervised Object Localization and Detection: A Survey (2021, unknown): “In [52], Gondal et al. apply the weakly supervised object detection network on the retina images and achieve few false positives with high sensitivity on the lesion-level prediction.”
A Deep Learning Ensemble Approach for Diabetic Retinopathy Detection (2019, background): “[18] proposed a CNN model for the referable Diabetic Retinopathy (RDR).”