Replication study: Development and validation of deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs

Replication studies are essential for validation of new methods, and are crucial to maintain the high standards of scientific publications, and to use the results in practice. We have attempted to replicate the main method in 'Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs' published in JAMA 2016; 316(22). We re-implemented the method since the source code is not available, and we used publicly available data sets. The original study used non-public fundus images from EyePACS and three hospitals in India for training. We used a different EyePACS data set from Kaggle. The original study used the benchmark data set Messidor-2 to evaluate the algorithm's performance. We used the same data set. In the original study, ophthalmologists re-graded all images for diabetic retinopathy, macular edema, and image gradability. There was one diabetic retinopathy grade per image for our data sets, and we assessed image gradability ourselves. Hyper-parameter settings were not described in the original study. But some of these were later published. We were not able to replicate the original study. Our algorithm's area under the receiver operating curve (AUC) of 0.94 on the Kaggle EyePACS test set and 0.80 on Messidor-2 did not come close to the reported AUC of 0.99 in the original study. This may be caused by the use of a single grade per image, different data, or different not described hyper-parameter settings. This study shows the challenges of replicating deep learning, and the need for more replication studies to validate deep learning methods, especially for medical image analysis. Our source code and instructions are available at: https://github.com/mikevoets/jama16-retina-replicationComment: The third version of this paper includes results from replication after certain hyper-parameters were published in later article. 16 pages, 6 figures, 1 table, presented at NOBIM 201

Zoom-in-Net: Deep Mining Lesions for Diabetic Retinopathy Detection

We propose a convolution neural network based algorithm for simultaneously diagnosing diabetic retinopathy and highlighting suspicious regions. Our contributions are two folds: 1) a network termed Zoom-in-Net which mimics the zoom-in process of a clinician to examine the retinal images. Trained with only image-level supervisions, Zoomin-Net can generate attention maps which highlight suspicious regions, and predicts the disease level accurately based on both the whole image and its high resolution suspicious patches. 2) Only four bounding boxes generated from the automatically learned attention maps are enough to cover 80% of the lesions labeled by an experienced ophthalmologist, which shows good localization ability of the attention maps. By clustering features at high response locations on the attention maps, we discover meaningful clusters which contain potential lesions in diabetic retinopathy. Experiments show that our algorithm outperform the state-of-the-art methods on two datasets, EyePACS and Messidor.Comment: accepted by MICCAI 201

Artificial Intelligence and Deep Learning-Based System Design for Diabetic Retinopathy Classification

One of the biggest causes of avoidable blindness throughout the world is diabetic retinopathy (DR). There is a significant unmet need to test all diabetes patients for DR, and many instances of DR go undetected and untreated. In order to automate DR screening, this research aimed to create reliable diagnostic technologies. In order to reduce the pace of vision loss, it is important to refer eyes suspected of having DR to an ophthalmologist for further assessment and treatment. The primary goal of this research is to improve the classification accuracy for Diabetic Retinopathy (DR). In this script, we present a new neural network model for DR forecasting. The suggested model's accuracy in identifying DR phases was measured against that of regular and ensemble-based models. Various benchmark datasets, including MESSIDOR, IDRID, and APTOS, are used in the studies. The suggested DRPNN algorithm outperformed the competition in experiments assessed using industry-standard criteria