Deep Learning for Multi-Label Disease Classification of Retinal Images: Insights from Brazilian Data for AI Development in Lower-Middle Income Countries

Gould, Dewi S.W.; Yang, Jenny; Clifton, David A.

doi:10.1101/2024.02.12.24302676

Cited by 3 publications

(3 citation statements)

References 23 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…The employment of advanced deep learning techniques, such as ConvNext V2, further underscores the dataset’s utility [ 35 ] and has yielded notable advancements in the field of retina fundus photo analysis. The results showcase the superiority of the architecture over the previously established benchmarks, primarily those set by ResNet 50 in the classification tasks of binary diabetic retinopathy and three-class diabetic retinopathy states [ 39 , 41 ].…”

Section: Discussionmentioning

confidence: 75%

“…We opted for ConvNext V2, diverging from the conventional use of ResNet-50 used in prior research on the BRSET dataset [ 39 – 41 ]. The reason for using ConvNext V2 instead of ResNet-50, is that ConvNext V2 is an evolution of the ResNet-50 architecture, and introduces several enhancements over its predecessors, such as layer normalization, expanded kernel sizes, or regularization techniques [ 35 , 42 ].…”

Section: Methodsmentioning

confidence: 99%

See 1 more Smart Citation

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos

Nakayama,

Restrepo,

Matos

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

Introduction The Brazilian Multilabel Ophthalmological Dataset (BRSET) addresses the scarcity of publicly available ophthalmological datasets in Latin America. BRSET comprises 16,266 color fundus retinal photos from 8,524 Brazilian patients, aiming to enhance data representativeness, serving as a research and teaching tool. It contains sociodemographic information, enabling investigations into differential model performance across demographic groups. Methods Data from three São Paulo outpatient centers yielded demographic and medical information from electronic records, including nationality, age, sex, clinical history, insulin use, and duration of diabetes diagnosis. A retinal specialist labeled images for anatomical features (optic disc, blood vessels, macula), quality control (focus, illumination, image field, artifacts), and pathologies (e.g., diabetic retinopathy). Diabetic retinopathy was graded using International Clinic Diabetic Retinopathy and Scottish Diabetic Retinopathy Grading. Validation used a ConvNext model trained during 50 epochs using a weighted cross entropy loss to avoid overfitting, with 70% training (20% validation), and 30% testing subsets. Performance metrics included area under the receiver operating curve (AUC) and Macro F1-score. Saliency maps were calculated for interpretability. Results BRSET comprises 65.1% Canon CR2 and 34.9% Nikon NF5050 images. 61.8% of the patients are female, and the average age is 57.6 (± 18.26) years. Diabetic retinopathy affected 15.8% of patients, across a spectrum of disease severity. Anatomically, 20.2% showed abnormal optic discs, 4.9% abnormal blood vessels, and 28.8% abnormal macula. A ConvNext V2 model was trained and evaluated BRSET in four prediction tasks: “binary diabetic retinopathy diagnosis (Normal vs Diabetic Retinopathy)” (AUC: 97, F1: 89); “3 class diabetic retinopathy diagnosis (Normal, Proliferative, Non-Proliferative)” (AUC: 97, F1: 82); “diabetes diagnosis” (AUC: 91, F1: 83); “sex classification” (AUC: 87, F1: 70). Discussion BRSET is the first multilabel ophthalmological dataset in Brazil and Latin America. It provides an opportunity for investigating model biases by evaluating performance across demographic groups. The model performance of three prediction tasks demonstrates the value of the dataset for external validation and for teaching medical computer vision to learners in Latin America using locally relevant data sources.

show abstract

Section: Discussionmentioning

confidence: 75%

Section: Methodsmentioning

confidence: 99%

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos

Nakayama,

Restrepo,

Matos

et al. 2024

PLOS Digit Health

View full text Add to dashboard Cite

show abstract

“…It leverages self-supervised pre-training on large-scale datasets, a multi-crop strategy, attention mechanisms, and scalability to learn rich and generalizable visual features. These architectures offer advantages over their predecessors, such as ResNets and the original ViT, which have been widely used in prior retinal imaging research [32][33][34] .…”

Section: Network Frameworkmentioning

confidence: 99%

mBRSET: A Portable Retina Fundus Photos Benchmark Dataset for Clinical and Demographic Prediction

Wu,

Restrepo,

Nakayama

et al. 2024

Preprint

View full text Add to dashboard Cite

This paper introduces mBRSET, the first publicly available retina dataset captured using handheld retinal cameras in real-life, high-burden scenarios, comprising 5,164 images from 1,291 patients of diverse backgrounds. This dataset addresses the lack of ophthalmological data in low- and middle-income countries (LMICs) by providing a cost-effective and accessible solution for ocular screening and management. Portable retinal cameras enable applications outside traditional hospital settings, such as community health screenings and telemedicine consultations, thereby democratizing healthcare. Extensive metadata that are typically unavailable in other datasets, including age, sex, diabetes duration, treatments, and comorbidities, are also recorded. To validate the utility of mBRSET, state-of-the-art deep models, including ConvNeXt V2, Dino V2, and SwinV2, were trained for benchmarking, achieving high accuracy in clinical tasks diagnosing diabetic retinopathy, and macular edema; and in fairness tasks predicting education and insurance status. The mBRSET dataset serves as a resource for developing AI algorithms and investigating real-world applications, enhancing ophthalmological care in resource-constrained environments.

show abstract

DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era

Restrepo,

Wu,

Vásquez-Venegas

et al. 2024

Preprint

View full text Add to dashboard Cite

In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare. This paper introduces a new process model for multimodal Data Fusion for Data Mining, integrating embeddings and the Cross-Industry Standard Process for Data Mining with the existing Data Fusion Information Group model. Our model aims to decrease computational costs, complexity, and bias while improving efficiency and reliability. We also propose "disentangled dense fusion," a novel embedding fusion method designed to optimize mutual information and facilitate dense inter-modality feature interaction, thereby minimizing redundant information.We demonstrate the model's efficacy through three use cases: predicting diabetic retinopathy using retinal images and patient metadata, domestic violence prediction employing satellite imagery, internet, and census data, and identifying clinical and demographic features from radiography images and clinical notes. The model achieved a Macro F1 score of 0.92 in diabetic retinopathy prediction, an R-squared of 0.854 and sMAPE of 24.868 in domestic violence prediction, and a macro AUC of 0.92 and 0.99 for disease prediction and sex classification, respectively, in radiological analysis. These results underscore the Data Fusion for Data Mining model's potential to significantly impact multimodal data processing, promoting its adoption in diverse, resource-constrained settings.

show abstract

Deep Learning for Multi-Label Disease Classification of Retinal Images: Insights from Brazilian Data for AI Development in Lower-Middle Income Countries

Cited by 3 publications

References 23 publications

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos

BRSET: A Brazilian Multilabel Ophthalmological Dataset of Retina Fundus Photos

mBRSET: A Portable Retina Fundus Photos Benchmark Dataset for Clinical and Demographic Prediction

DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era

Contact Info

Product

Resources

About