Jove
Visualize
Contact Us
JoVE
x logofacebook logolinkedin logoyoutube logo
ABOUT JoVE
OverviewLeadershipBlogJoVE Help Center
AUTHORS
Publishing ProcessEditorial BoardScope & PoliciesPeer ReviewFAQSubmit
LIBRARIANS
TestimonialsSubscriptionsAccessResourcesLibrary Advisory BoardFAQ
RESEARCH
JoVE JournalMethods CollectionsJoVE Encyclopedia of ExperimentsArchive
EDUCATION
JoVE CoreJoVE BusinessJoVE Science EducationJoVE Lab ManualFaculty Resource CenterFaculty Site
Terms & Conditions of Use
Privacy Policy
Policies

Related Concept Videos

You might also read

Related Articles

Articles linked to this work by shared authors, journal, and citation graph.

Sort by
Same author

Distinct Proteasomal Pathways Drive Oncogenic PPM1D Activation.

bioRxiv : the preprint server for biology·2026
Same author

Synergistic Action of Antimicrobial Peptides and Antibiotics.

International journal of molecular sciences·2026
Same author

Overcoming Domain Shift in Atypical Mitotic Figure Detection with Deep Ensemble Learning.

Studies in health technology and informatics·2026
Same author

Comparison of Loss Functions for Fibroglandular Tissue Segmentation in MRI.

Studies in health technology and informatics·2026
Same author

German Clinical Practice Guideline on Microhematuria in Children and Young Adults: Evaluating Early Detection of Kidney Disease.

Kidney international reports·2026
Same author

Development and External Validation of a Deep Learning Model to Predict Mortality in Aneurysmal Subarachnoid Hemorrhage Using Admission Computed Tomography.

Neurosurgery·2026
Same journal

Generative morphodynamic forecasting enables robust zero-shot volumetric medical segmentation.

Medical image analysis·2026
Same journal

ContiMorph: An unsupervised learning framework for cardiac motion tracking with time-continuous diffeomorphism.

Medical image analysis·2026
Same journal

MedP-CLIP: Medical CLIP with region-aware prompt integration.

Medical image analysis·2026
Same journal

Multi-organ guided diagnosis of mild cognitive impairment via hierarchical alignment and knowledge distillation.

Medical image analysis·2026
Same journal

SUDA: Simultaneous unsupervised knowledge distillation and adaptation of foundation models for efficient pathological image analysis.

Medical image analysis·2026
Same journal

Beyond the LUMIR challenge: The pathway to foundational registration models.

Medical image analysis·2026
See all related articles

Related Experiment Video

Updated: Jun 10, 2025

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.6K

RFMiD: Retinal Image Analysis for multi-Disease Detection challenge.

Samiksha Pachade1, Prasanna Porwal1, Manesh Kokare1

  • 1Center of Excellence in Signal and Image Processing, Shri Guru Gobind Singhji Institute of Engineering and Technology, Nanded 431606, India.

Medical Image Analysis
|October 12, 2024
PubMed
Summary
This summary is machine-generated.

A new challenge and dataset (RFMiD) were created to detect both common and rare retinal diseases using deep learning. Top methods combined data preprocessing, augmentation, and ensembling for better multi-disease screening.

Keywords:
ClassificationMulti-label classificationOcular diseaseRare pathology detectionRetinal fundus images

More Related Videos

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies
06:16

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Published on: July 28, 2023

2.4K
Retinal Pathophysiological Evaluation in a Rat Model
09:11

Retinal Pathophysiological Evaluation in a Rat Model

Published on: May 6, 2022

4.5K

Related Experiment Videos

Last Updated: Jun 10, 2025

Using Retinal Imaging to Study Dementia
09:17

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

21.6K
Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies
06:16

Author Spotlight: Unraveling the Pathogenesis of Age-Related Macular Degeneration and Discovering Potential Therapies

Published on: July 28, 2023

2.4K
Retinal Pathophysiological Evaluation in a Rat Model
09:11

Retinal Pathophysiological Evaluation in a Rat Model

Published on: May 6, 2022

4.5K

Area of Science:

  • Ophthalmology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Existing deep learning models for retinal disease diagnosis focus on common pathologies like diabetic retinopathy, glaucoma, and age-related macular degeneration.
  • Computer-aided diagnosis systems often overlook rare but sight-threatening conditions, limiting their clinical adoption by ophthalmologists.
  • There is a need for automated systems capable of detecting a wider spectrum of retinal diseases, including both frequent and rare conditions.

Purpose of the Study:

  • To advance the state-of-the-art in automatic ocular disease classification by including rare pathologies alongside common ones.
  • To organize a grand challenge, "Retinal Image Analysis for multi-Disease Detection" (ISBI 2021), to foster research in this area.
  • To introduce a new dataset, the "Retinal Fundus Multi-disease Image Dataset" (RFMiD), for training and evaluating multi-disease detection models.

Main Methods:

  • The challenge featured two sub-challenges: binary disease screening and 28-class multi-label disease classification.
  • A new dataset, RFMiD, comprising fundus images for multi-disease detection was utilized.
  • Top-performing solutions involved a combination of data preprocessing, data augmentation, pre-trained models, and model ensembling.

Main Results:

  • The challenge received 74 submissions, indicating significant interest from the scientific community.
  • The top methodologies demonstrated the effectiveness of combining various AI techniques for multi-disease detection.
  • The results pave the way for more generalizable retinal screening models that account for both common and rare diseases.

Conclusions:

  • The developed multi-disease detection approach, incorporating rare pathologies, enables the creation of more comprehensive retinal screening tools.
  • This work moves beyond single-disease detection systems, addressing a critical gap in current automated ophthalmology tools.
  • The success of the challenge and the performance of the top methods highlight the potential of AI in improving the scope and accuracy of retinal disease diagnosis.