Multi-retinal disease classification by reduced deep learning features

Arunkumar, R.; Karthigaikumar, P.

doi:10.1007/s00521-015-2059-9

Cited by 74 publications

(35 citation statements)

References 23 publications

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“…Background of the disease: As people with diabetes have a high prevalence for RDR [47], a frequent retinal screening is recommended and deep learning algorithms have been successfully developed to classify fundus images ( [8], [20], [3], [46]). The black box character of these algorithms can be reduced by visual explanation techniques as shown in [18].…”

Section: A42 Visual Explanation For Medical Imagesmentioning

confidence: 99%

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Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks

Wagner

Köhler

Gindele

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

128

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To verify and validate networks, it is essential to gain insight into their decisions, limitations as well as possible shortcomings of training data. In this work, we propose a post-hoc, optimization based visual explanation method, which highlights the evidence in the input image for a specific prediction. Our approach is based on a novel technique to defend against adversarial evidence (i.e. faulty evidence due to artefacts) by filtering gradients during optimization. The defense does not depend on human-tuned parameters. It enables explanations which are both fine-grained and preserve the characteristics of images, such as edges and colors. The explanations are interpretable, suited for visualizing detailed evidence and can be tested as they are valid model inputs. We qualitatively and quantitatively evaluate our approach on a multitude of models and datasets.

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Section: A42 Visual Explanation For Medical Imagesmentioning

confidence: 99%

“…A value of zero for these thus corresponds to a grey color (i.e. the color of the data mean) 3. Fig.…”

mentioning

confidence: 99%

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks

Wagner

Köhler

Gindele

et al. 2019

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)

128

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“…While first approaches using neural networks to detect diabetic retinopathy on retinal images without additional feature extraction showed a low classification accuracy [5,6], recent approaches based on deep neural networks [7,8,9] report good performance. For medical experts, these algorithms represent black box approaches as only a classification result but no information to why this conclusion is reached is provided.…”

Section: Introductionmentioning

confidence: 99%

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

Gondal

Köhler

Grzeszick

et al. 2017

2017 IEEE International Conference on Image Processing (ICIP)

137

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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.

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“…Networks have also been employed for image-level DME diagnosis. Arunkumar and Karthigaikumar [29] used a DBN for feature extraction and a multiclass SVM for classification to diagnose AMD together with other DR complications. In this method, fundus images first undergo a preprocessing procedure that includes normalization, contrast adjustment or histogram equalization.…”

Section: Deep Beliefmentioning

confidence: 99%

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey

Asiri

Hussain

Adel

et al. 2019

Artificial Intelligence in Medicine

225

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Diabetic retinopathy (DR) results in vision loss if not treated early. A computer-aided diagnosis (CAD) system based on retinal fundus images is an efficient and effective method for early DR diagnosis and assisting experts. A computer-aided diagnosis (CAD) system involves various stages like detection, segmentation and classification of lesions in fundus images. Many traditional machine-learning (ML) techniques based on hand-engineered features have been introduced. The recent emergence of deep learning (DL) and its decisive victory over traditional ML methods for various applications motivated the researchers to employ it for DR diagnosis, and many deeplearning-based methods have been introduced. In this paper, we review these methods, highlighting their pros and cons. In addition, we point out the challenges to be addressed in designing and learning about efficient, effective and robust deep-learning algorithms for various problems in DR diagnosis and draw attention to directions for future research.

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Multi-retinal disease classification by reduced deep learning features

Cited by 74 publications

References 23 publications

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks

Interpretable and Fine-Grained Visual Explanations for Convolutional Neural Networks

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

Deep learning based computer-aided diagnosis systems for diabetic retinopathy: A survey

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