Development of a deep residual learning algorithm to screen for glaucoma from fundus photography

Shibata, Naomi; Tanito, Masaki; Mitsuhashi, Keita; Fujino, Yuri; Matsuura, Masato; Murata, Hiroshi; Asaoka, Ryo

doi:10.1038/s41598-018-33013-w

Cited by 200 publications

(160 citation statements)

References 31 publications

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“…[42] presented a novel ensemble network based on the application of different CNNs to the global fundus image and to different versions of optic disc region. The assessment of deep learning algorithms with transfer learning has also been addressed in [43][44][45] implementing studies with greater number of images than previous works and achieving expert level accuracy and high sensitivity and specificity. Finally, in OCT there are also recent studies applying CNNs for glaucoma detection [46] or segmentation of layers [47,48].…”

Section: Fig 1 Of the With G Optic Inferio Tempomentioning

confidence: 99%

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Gómez-Valverde

Antón

Fatti

et al. 2019

Biomed. Opt. Express

182

120

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Glaucoma detection in color fundus images is a challenging task that requires expertise and years of practice. In this study we exploited the application of different Convolutional Neural Networks (CNN) schemes to show the influence in the performance of relevant factors like the data set size, the architecture and the use of transfer learning vs newly defined architectures. We also compared the performance of the CNN based system with respect to human evaluators and explored the influence of the integration of images and data collected from the clinical history of the patients. We accomplished the best performance using a transfer learning scheme with VGG19 achieving an AUC of 0.94 with sensitivity and specificity ratios similar to the expert evaluators of the study. The experimental results using three different data sets with 2313 images indicate that this solution can be a valuable option for the design of a computer aid system for the detection of glaucoma in large-scale screening programs.

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Section: Fig 1 Of the With G Optic Inferio Tempomentioning

confidence: 99%

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Gómez-Valverde

Antón

Fatti

et al. 2019

Biomed. Opt. Express

182

120

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“…; Shibata et al. ). In 2015, the first results on glaucoma classification with deep learning were published, using two data sets (<2000 images) (Chen et al.…”

Section: Introductionmentioning

confidence: 97%

Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning

Hemelings

Elen

Breda

et al. 2019

Acta Ophthalmologica

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Purpose: To assess the use of deep learning (DL) for computer-assisted glaucoma identification, and the impact of training using images selected by an active learning strategy, which minimizes labelling cost. Additionally, this study focuses on the explainability of the glaucoma classifier. Methods: This original investigation pooled 8433 retrospectively collected and anonymized colour optic disc-centred fundus images, in order to develop a deep learning-based classifier for glaucoma diagnosis. The labels of the various deep learning models were compared with the clinical assessment by glaucoma experts. Data were analysed between March and October 2018. Sensitivity, specificity, area under the receiver operating characteristic curve (AUC), and amount of data used for discriminating between glaucomatous and nonglaucomatous fundus images, on both image and patient level. Results: Trained using 2072 colour fundus images, representing 42% of the original training data, the trained DL model achieved an AUC of 0.995, sensitivity and specificity of, respectively, 98.0% (CI 95.5%-99.4%) and 91% (CI 84.0%-96.0%), for glaucoma versus non-glaucoma patient referral. Conclusions: These results demonstrate the benefits of deep learning for automated glaucoma detection based on optic disc-centred fundus images. The combined use of transfer and active learning in the medical community can optimize performance of DL models, while minimizing the labelling cost of domain-specific mavens. Glaucoma experts are able to make use of heat maps generated by the deep learning classifier to assess its decision, which seems to be related to inferior and superior neuroretinal rim (within ONH), and RNFL in superotemporal and inferotemporal zones (outside ONH).

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“…The reason is that large datasets of images have been collected within DR screening programs for diabetic patients over the past decades (Massin et al, 2008;Cuadros and Bresnick, 2009): those images were interpreted by human readers, which allows efficient training of supervised deep learning classifiers. Automatic screening systems were also proposed for glaucoma (Li et al, 2018;Shibata et al, 2018;Christopher et al, 2018;Phan et al, * LaTIM -IBRBS -22, avenue Camille Desmoulins -29200 Brest, France -Tel. : +33 2 98 01 81 29…”

Section: Introductionmentioning

confidence: 99%

Automatic detection of rare pathologies in fundus photographs using few-shot learning

Quellec

Lamard

Conze

et al. 2020

Medical Image Analysis

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In the last decades, large datasets of fundus photographs have been collected in diabetic retinopathy (DR) screening networks. Through deep learning, these datasets were used to train automatic detectors for DR and a few other frequent pathologies, with the goal to automate screening. One challenge limits the adoption of such systems so far: automatic detectors ignore rare conditions that ophthalmologists currently detect. The reason is that standard deep learning requires too many examples of these conditions. To address this limitation, we propose a new machine learning (ML) framework, called spin-off learning, for the automatic detection of rare conditions, like papilledema or anterior ischemic optic neuropathy. This framework extends convolutional neural networks (CNNs), trained for frequent conditions, with an unsupervised probabilistic model for rare condition detection. Spin-off learning is based on the observation that CNNs often perceive photographs containing the same anomalies as similar, even though these CNNs were trained to detect unrelated conditions. This observation was based on the t-SNE visualization tool, which we decided to incorporate in our probabilistic model. Spin-off learning supports heatmap generation, so the detected anomalies can be highlighted in images for decision support. Experiments in a dataset of 164,660 screening examinations from the OPHDIAT screening network show that spin-off learning can detect 37 conditions, out of 41, with an area under the ROC curve (AUC) greater than 0.8 (average AUC: 0.938). In particular, spin-off learning significantly outperforms other candidate ML frameworks for detecting rare conditions, including multitask learning, transfer learning and one-shot learning. We expect these richer predictions to trigger the adoption of automated eye pathology screening, which will revolutionize clinical practice in ophthalmology.

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Development of a deep residual learning algorithm to screen for glaucoma from fundus photography

Cited by 200 publications

References 31 publications

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Automatic glaucoma classification using color fundus images based on convolutional neural networks and transfer learning

Accurate prediction of glaucoma from colour fundus images with a convolutional neural network that relies on active and transfer learning

Automatic detection of rare pathologies in fundus photographs using few-shot learning

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