The DOI matches its Crossref record; 31 of the 31 references it cites were found in a registry or an index.
Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research
Open accessAbstract
The world faces difficulties in terms of eye care, including treatment, quality of prevention, vision rehabilitation services, and scarcity of trained eye care experts. Early detection and diagnosis of ocular pathologies would enable forestall of visual impairment. One challenge that limits the adoption of computer-aided diagnosis tool by ophthalmologists is the number of sight-threatening rare pathologies, such as central retinal artery occlusion or anterior ischemic optic neuropathy, and others are usually ignored. In the past two decades, many publicly available datasets of color fundus images have been collected with a primary focus on diabetic retinopathy, glaucoma, age-related macular degeneration and few other frequent pathologies. To enable development of methods for automatic ocular disease classification of frequent diseases along with the rare pathologies, we have created a new Retinal Fundus Multi-disease Image Dataset (RFMiD). It consists of 3200 fundus images captured using three different fundus cameras with 46 conditions annotated through adjudicated consensus of two senior retinal experts. To the best of our knowledge, our dataset, RFMiD, is the only publicly available dataset that constitutes such a wide variety of diseases that appear in routine clinical settings. This dataset will enable the development of generalizable models for retinal screening.
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Cites
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Age-related macular degeneration
Lim, L. S., Mitchell, P., Seddon, J. M., Holz, F. G., & Wong, T. Y. (2012) · The Lancet · doi:10.1016/s0140-6736(12)60282-7 · Cited by 1,717
FEEDBACK ON A PUBLICLY DISTRIBUTED IMAGE DATABASE: THE MESSIDOR DATABASE
Decencière, E., Zhang, X., Cazuguel, G., Lay, B., Cochener, B., Trone, C., Gain, P., Ordonez, R., Massin, P., Erginay, A., et al. (2014) · Image Analysis & Stereology · doi:10.5566/ias.1155 · Cited by 1,424
Wong, T. Y., Cheung, C. M. G., Larsen, M., Sharma, S., & Simó, R. (2016) · Nature Reviews Disease Primers · doi:10.1038/nrdp.2016.12 · Cited by 1,094
Porwal, P., Pachade, S., Kamble, R., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., & Meriaudeau, F. (2018) · Data · doi:10.3390/data3030025 · Cited by 923
Self-citationInternational Photographic Classification and Grading System for Myopic Maculopathy
Ohno-Matsui, K., Kawasaki, R., Jonas, J. B., Cheung, C. M. G., Saw, S.-M., Verhoeven, V. J. M., Klaver, C. C. W., Moriyama, M., Shinohara, K., Kawasaki, Y., et al. (2015) · American Journal of Ophthalmology · doi:10.1016/j.ajo.2015.01.022 · Cited by 866
Orlando, J. I., Fu, H., Barbosa Breda, J., van Keer, K., Bathula, D. R., Diaz-Pinto, A., Fang, R., Heng, P.-A., Kim, J., Lee, J., et al. (2020) · Medical Image Analysis · doi:10.1016/j.media.2019.101570 · Cited by 855
Retinitis Pigmentosa: Genes and Disease Mechanisms
Parmeggiani, F., S. Sorrentino, F., Ponzin, D., Barbaro, V., Ferrari, S., & Di Iorio, E. (2011) · Current Genomics · doi:10.2174/138920211795860107 · Cited by 627
RIM-ONE: An open retinal image database for optic nerve evaluation
Fumero, F., Alayon, S., Sanchez, J. L., Sigut, J., & Gonzalez-Hernandez, M. (2011) · 2011 24th International Symposium on Computer-Based Medical Systems (CBMS) · doi:10.1109/cbms.2011.5999143 · Cited by 538
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Advances in medical image analysis with vision Transformers: A comprehensive review
Azad, R., Kazerouni, A., Heidari, M., Aghdam, E. K., Molaei, A., Jia, Y., Jose, A., Roy, R., & Merhof, D. (2024) · Medical Image Analysis · doi:10.1016/j.media.2023.103000 · Cited by 485
Li, J., Chen, J., Tang, Y., Wang, C., Landman, B. A., & Zhou, S. K. (2023) · Medical Image Analysis · doi:10.1016/j.media.2023.102762 · Cited by 389
Deep Learning Techniques for Diabetic Retinopathy Classification: A Survey
Atwany, M. Z., Sahyoun, A. H., & Yaqub, M. (2022) · IEEE Access · doi:10.1109/access.2022.3157632 · Cited by 266
ContIG: Self-supervised Multimodal Contrastive Learning for Medical Imaging with Genetics
Taleb, A., Kirchler, M., Monti, R., & Lippert, C. (2022) · 2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr52688.2022.02024 · Cited by 83
Few-Shot Class-Incremental Learning for Retinal Disease Recognition
Zhang, J., Zhao, P., Zhao, Y., Li, C., & Hu, D. (2025) · IEEE Journal of Biomedical and Health Informatics · doi:10.1109/jbhi.2024.3457915 · Cited by 23
Metadata
- Authors
- Pachade, S., Porwal, P., Thulkar, D., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., Giancardo, L., Quellec, G., & Mériaudeau, F.
- Venue
- Data
- Publisher
- MDPI AG
- Published
- 2021-02-03
- Volume
- 6
- Issue
- 2
- Pages
- 14
- ISSN
- 2306-5729 (ISSN-L)
- OpenAlex
- W3128220181
- Cited by
- 285
- References
- 31
- FWCI
- 18.34
- Type
- journal article
- Language
- en
- Open access
- gold
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- Pachade, S., Porwal, P., Thulkar, D., Kokare, M., Deshmukh, G., Sahasrabuddhe, V., Giancardo, L., Quellec, G., & Mériaudeau, F. (2021). Retinal Fundus Multi-Disease Image Dataset (RFMiD): A Dataset for Multi-Disease Detection Research. Data. https://doi.org/10.3390/data6020014
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