Search research articles

ABOUT JoVE

Overview Leadership Blog JoVE Help Center

AUTHORS

Publishing Process Editorial Board Scope & Policies Peer Review FAQ Submit

LIBRARIANS

Testimonials Subscriptions Access Resources Library Advisory Board FAQ

RESEARCH

JoVE Journal Methods Collections JoVE Encyclopedia of Experiments Archive

EDUCATION

JoVE Core JoVE Business JoVE Science Education JoVE Lab Manual Faculty Resource Center Faculty Site

Terms & Conditions of Use

Related Concept Videos

The Retina

The Retina

The retina is a layer of nervous tissue at the back of the eye that transduces light into neural signals. This process, called phototransduction, is carried out by rod and cone photoreceptor cells in the back of the retina.

You might also read

Related Articles

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

Sort by

Same author

Cross Domain Self-Prompting SAM2 for Intraoperative OCT Video Segmentation.

IEEE journal of biomedical and health informatics·2026

Same author

Establishment and validation of a predictive scoring system for titanium clip retention at six months after endoscopic submucosal dissection for gastric lesions.

Scientific reports·2026

Same author

Streamlining stereo-differentiable rendering for marker-free real-time tracking of surgical robots.

International journal of computer assisted radiology and surgery·2026

Same author

SARS-CoV-2 infection and vaccination elicit distinct pharyngeal mucosal B cell responses in children.

Nature communications·2026

Same author

Bio-inspired cellular aerogel fibers integrating high mechanical strength and softness for thermal insulation textiles.

Nature communications·2026

Same author

ROBUST-MIPS: A Combined Skeletal Pose and Instance Segmentation Dataset for Laparoscopic Surgical Instruments.

Scientific data·2026

Search research articles

Related Experiment Video

Updated: Jan 18, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

RetiGen: Framework leveraging domain generalization and test-time adaptation for multi-view retinal diagnostics.

Gongyu Zhang¹, Ze Chen², Jiayu Huo³

¹School of Biomedical Engineering & Imaging Sciences, King's College London, London, United Kingdom; School of Life Course & Population Sciences, King's College London, London, United Kingdom.

Computers in Biology and Medicine

|September 11, 2025

Summary

RetiGen integrates domain generalization and test-time adaptation for ophthalmic imaging, significantly improving diagnostic accuracy across different datasets. This novel approach enhances machine learning model robustness and addresses domain shift challenges.

Keywords:

Deep learning Domain generalization Medical image analysis Multi-view imaging Retinal diagnostics Test-time adaptation

More Related Videos

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Related Experiment Videos

Last Updated: Jan 18, 2026

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Development of a Gaze-Contingent Display Framework Designed for Perceptual and Oculomotor Research with Simulated Central Vision Loss

Published on: April 11, 2025

Using Retinal Imaging to Study Dementia

Using Retinal Imaging to Study Dementia

Published on: November 6, 2017

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Author Spotlight: Insights into Visual Cortex Research Through Wide-View fMRI Mapping

Published on: December 8, 2023

Area of Science:

Ophthalmic imaging
Medical machine learning
Computer vision

Background:

Domain generalization (DG) and test-time adaptation (TTA) improve medical imaging model accuracy across different domains.
Existing methods often address DG or TTA separately, not leveraging their combined strengths.
Domain shift remains a significant challenge, hindering the performance of AI in diagnostics.

Purpose of the Study:

To introduce RetiGen, a novel test-time optimization framework that integrates DG and TTA in an end-to-end fashion.
To enhance the robustness and accuracy of machine learning models in the ophthalmic imaging domain, specifically using multi-view color fundus photographs.
To address the persistent issue of domain shift in medical imaging analysis.

Main Methods:

RetiGen is a test-time optimization framework designed for integration with existing domain generalization approaches.
It utilizes unlabeled multi-view color fundus photographs, leveraging information from multiple viewing angles.
The framework integrates class balancing, test-time adaptation, and a multi-view optimization strategy to combat domain shift.

Main Results:

RetiGen significantly enhances the generalizability and accuracy of ophthalmic imaging models.
On the MFIDDR dataset, RetiGen improved AUC from 0.751 to 0.872 (0.121 improvement).
On the DRTiD dataset, RetiGen improved AUC from 0.794 to 0.879 (0.085 improvement), outperforming state-of-the-art methods.

Conclusions:

RetiGen effectively addresses domain shift by combining DG and TTA in an end-to-end framework.
The proposed method demonstrates superior performance compared to existing state-of-the-art techniques in both DG and TTA.
RetiGen offers a promising solution for improving diagnostic accuracy in ophthalmic imaging and other medical domains.