Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research

Pachade, Samiksha; Porwal, Prasanna; Thulkar, Dhanshree; Kokare, Manesh; Deshmukh, Girish; Sahasrabuddhe, Vivek; Giancardo, Luca; Quellec, Gwénolé; Mériaudeau, Fabrice

doi:10.3390/data6020014

Cited by 132 publications

(51 citation statements)

References 51 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…As future work, we are interested in validating the medical gain of our pipeline for automated multi-disease detection in retinal imaging as clinical decision support through a clinical study. occlusion (CRVO), tortuous vessels (TV), asteroid hyalosis (AH), optic disc pallor (ODP), optic disc edema (ODE), shunt (ST), anterior ischemic optic neuropathy (AION), parafoveal telangiectasia (PT), retinal traction (RT), retinitis (RS), chorioretinitis (CRS), exudation (EDN), retinal pigment epithelium changes (RPEC), macular hole (MHL), retinitis pigmentosa (RP), cotton wool spots (CWS), coloboma (CB), optic disc pit maculopathy (ODPM), preretinal hemorrhage (PRH), myelinated nerve fibers (MNF), hemorrhagic retinopathy (HR), central retinal artery occlusion (CRAO), tilted disc (TD), cystoid macular edema (CME), post traumatic choroidal rupture (PTCR), choroidal folds (CF), vitreous hemorrhage (VH), macroaneurysm (MCA), vasculitis (VS), branch retinal artery occlusion (BRAO), plaque (PLQ), hemorrhagic pigment epithelial detachment (HPED) and collateral (CL) For more information and details on the dataset, we refer to Pachade et al [7,8].…”

Section: Discussionmentioning

confidence: 99%

“…The RFMiD dataset consists of 3,200 retinal images for which 1,920 images were used as training dataset [7]. The fundus images were captured by three different fundus cameras having a resolution of 4288x2848 (277 images), 2048x1536 (150 images) and 2144x1424 (1,493 images), respectively.…”

Section: Retinal Imaging Datasetmentioning

confidence: 99%

“…These 46 conditions are represented by the following classes, which are also listed in Table 1: An overall normal/abnormal class, 27 specific condition classes and 1 'OTHER' class consisting of the remaining extremely rare conditions. Besides the training dataset, the organizers of the RIADD challenge hold 1,280 images back for external validation and testing datasets to ensure robust evaluation [7,8].…”

Section: Retinal Imaging Datasetmentioning

confidence: 99%

See 2 more Smart Citations

Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous Deep Learning Models

Müller

Soto-Rey

2021

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

Preventable or undiagnosed visual impairment and blindness affect billion of people worldwide. Automated multi-disease detection models offer great potential to address this problem via clinical decision support in diagnosis. In this work, we proposed an innovative multi-disease detection pipeline for retinal imaging which utilizes ensemble learning to combine the predictive capabilities of several heterogeneous deep convolutional neural network models. Our pipeline includes state-of-the-art strategies like transfer learning, class weighting, real-time image augmentation and Focal loss utilization. Furthermore, we integrated ensemble learning techniques like heterogeneous deep learning models, bagging via 5-fold cross-validation and stacked logistic regression models. Through internal and external evaluation, we were able to validate and demonstrate high accuracy and reliability of our pipeline, as well as the comparability with other state-of-the-art pipelines for retinal disease prediction.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Retinal Imaging Datasetmentioning

confidence: 99%

Section: Retinal Imaging Datasetmentioning

confidence: 99%

See 1 more Smart Citation

Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous Deep Learning Models

Müller

Soto-Rey

2021

Studies in Health Technology and Informatics

View full text Add to dashboard Cite

show abstract

“…In the future, we will work on other retinal diseases such as retinal detachment using fundus images deploying data augmentation methods such as elastic/plastic deformations as well as other DL-based architectures such as graph convolutional networks. Eye diseases such as age-related macular degeneration, media haze, drusen, myopia, branch retinal vein occlusion, tessellation, epiretinal membrane, laser scars, macular scar, central serous retinopathy, optic disc cupping, central retinal vein occlusion, tortuous vessels, asteroid hyalosis, optic disc pallor, optic disc edema, optociliary shunt, anterior ischemic optic neuropathy, parafoveal telangiectasia, retinal traction, retinitis, chorioretinitis, exudation, retinal pigment epithelium changes, macular hole, retinitis pigmentosa, and many other eye diseases [64] are affecting a large number of people worldwide, and their accurate and early detection using DL-based methods may allow for palliative care procedures employed by clinicians and medical practitioners.…”

Section: Discussionmentioning

confidence: 99%

Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples

Tufail

Ullah

Khan

et al. 2021

Wireless Communications and Mobile Computing

View full text Add to dashboard Cite

Diabetic retinopathy (DR) is a worldwide problem associated with the human retina. It leads to minor and major blindness and is more prevalent among adults. Automated screening saves time of medical care specialists. In this work, we have used different deep learning (DL) based 3D convolutional neural network (3D-CNN) architectures for binary and multiclass (5 classes) classification of DR. We have considered mild, moderate, no, proliferate, and severe DR categories. We have deployed two artificial data augmentation/enhancement methods: random weak Gaussian blurring and random shift along with their combination to accomplish these tasks in the spatial domain. In the binary classification case, we have found the performance of 3D-CNN architecture trained by deploying combined augmentation methods to be the best, while in the multiclass case, the performance of model trained without augmentation is the best. It is observed that the DL algorithms working with large volumes of data may achieve better performances as compared to the methods working with small volumes of data.

show abstract

“…However, because the amount of data was small, we also used publicly accessible web data to improve model generalizability including the Retinal Fundus Multi-Disease Image Dataset and other studies providing fundus photographs of posterior serous retinal detachment. 16 , 17 In particular, the Retinal Fundus Multi-Disease Image Dataset had 98 fundus photographs of patients with CSC, which were labeled by two ophthalmologists. This process also aimed to further de-identify the materials.…”

Section: Methodsmentioning

confidence: 99%

Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography

Yoo

Kim

Jeong

et al. 2022

Trans. Vis. Sci. Tech.

View full text Add to dashboard Cite

Purpose Central serous chorioretinopathy (CSC) is a retinal disease that frequently shows resolution and recurrence with serous detachment of the neurosensory retina. Here, we present a deep learning analysis of subretinal fluid (SRF) lesion segmentation in fundus photographs to evaluate CSC. Methods We collected 194 fundus photographs of SRF lesions from the patients with CSC. Three graders manually annotated of the entire SRF area in the retinal images. The dataset was randomly separated into training (90%) and validation (10%) datasets. We used the U-Net segmentation model based on conditional generative adversarial networks (pix2pix) to detect the SRF lesions. The algorithms were trained and validated using Google Colaboratory. Researchers did not need prior knowledge of coding skills or computing resources to implement this code. Results The validation results showed that the Jaccard index and Dice coefficient scores were 0.619 and 0.763, respectively. In most cases, the segmentation results overlapped with most of the reference areas in the annotated images. However, cases with exceptional SRFs were not accurate in terms of prediction. Using Colaboratory, the proposed segmentation task ran easily in a web-based environment without setup or personal computing resources. Conclusions The results suggest that the deep learning model based on U-Net from the pix2pix algorithm is suitable for the automatic segmentation of SRF lesions to evaluate CSC. Translational Relevance Our code implementation has the potential to facilitate ophthalmology research; in particular, deep learning–based segmentation can assist in the development of pathological lesion detection solutions.

show abstract

Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research

Cited by 132 publications

References 51 publications

Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous Deep Learning Models

Multi-Disease Detection in Retinal Imaging Based on Ensembling Heterogeneous Deep Learning Models

Diagnosis of Diabetic Retinopathy through Retinal Fundus Images and 3D Convolutional Neural Networks with Limited Number of Samples

Simple Code Implementation for Deep Learning–Based Segmentation to Evaluate Central Serous Chorioretinopathy in Fundus Photography

Contact Info

Product

Resources

About