2 citations · 6 across the 17 of their papers we have counts for
18 papers
Prediction bias in biological ageing markers
Yiqun Lin, Ariel Yuhan Ong, Matthew Yu Heng Wong +52
Biological ageing markers have attracted growing interest, with models estimating age from organ imaging or blood biomarkers. An estimated age above chronological age or the age-sp…
Beyond Natural-Image Foundation Models: Benchmarking Satellite Pretraining for Ophthalmic Image Analysis
Lovre Antonio Budimir, Mingya Alexa Gong, Alyssa Foong Quinney +5
Vision Foundation Models (VFMs) have emerged as a promising approach in medical imaging, producing broadly applicable systems that can be efficiently adapted across diverse imaging…
Representation Transfer of Foundation Models for Ultra-Widefield Retinal Imaging
Mingya Alexa Gong, Da Ma, Lovre Antonio Budimir +7
Despite the widespread adoption of foundation models as feature extractors for medical imaging, relatively little is understood about how different pretraining strategies influence…
PC-Seg: Progressive Cross-View Consistency for 3D OCT Segmentation from Sparse 2D Annotations
Tsubasa Konno, Takahiro Ninomiya, Yukun Zhou +6
Volumetric segmentation of optical coherence tomography (OCT) images is essential for diagnosing ocular diseases but requires labor-intensive voxel-wise annotations. While semi-sup…
Vibe coding for clinicians: democratising bespoke software development for digital health innovation
Ariel Yuhan Ong, Iain Livingstone, Caroline Kilduff +5
Clinicians often face workflow problems that are perceived as either too bespoke or low stakes to attract commercial attention. Historically, most do not have the technical knowled…
Deliberative multi-agent large language models improve clinical reasoning in ophthalmology
Ehsan Misaghi, Sean T Berkowitz, Bing Yu Chen +10
Large language models (LLMs) show potential for ophthalmic clinical reasoning, yet individual models risk introducing harm. We evaluated whether multi-agent LLM deliberative counci…
oculomix: Hierarchical Sampling for Retinal-Based Systemic Disease Prediction
Hyunmin Kim, Yukun Zhou, Rahul A. Jonas +4
Oculomics - the concept of predicting systemic diseases, such as cardiovascular disease and dementia, through retinal imaging - has advanced rapidly due to the data efficiency of t…
Native Intelligence Emerges from Large-Scale Clinical Practice: A Retinal Foundation Model with Deployment Efficiency
Jia Guo, Jiawei Du, Shengzhu Yang +21
Current retinal foundation models remain constrained by curated research datasets that lack authentic clinical context, and require extensive task-specific optimization for each ap…
Complementary Human-AI Clinical Reasoning in Ophthalmology
Mertcan Sevgi, Fares Antaki, Abdullah Zafar Khan +26
Vision impairment and blindness are a major global health challenge where gaps in the ophthalmology workforce limit access to specialist care. We evaluate AMIE, a medically fine-tu…
Generalist versus Specialist Vision Foundation Models for Ocular Disease and Oculomics
Yukun Zhou, Paul Nderitu, Jocelyn Hui Lin Goh +20
Medical foundation models, pre-trained with large-scale clinical data, demonstrate strong performance in diverse clinically relevant applications. RETFound, trained on nearly one m…
FusionFM: Fusing Eye-specific Foundational Models for Optimized Ophthalmic Diagnosis
Ke Zou, Jocelyn Hui Lin Goh, Yukun Zhou +11
Foundation models (FMs) have shown great promise in medical image analysis by improving generalization across diverse downstream tasks. In ophthalmology, several FMs have recently…
Performance of GPT-5 Frontier Models in Ophthalmology Question Answering
Fares Antaki, David Mikhail, Daniel Milad +11
Large language models (LLMs) such as GPT-5 integrate advanced reasoning capabilities that may improve performance on complex medical question-answering tasks. For this latest gener…
Delving into Out-of-Distribution Detection with Medical Vision-Language Models
Lie Ju, Sijin Zhou, Yukun Zhou +4
Recent advances in medical vision-language models (VLMs) demonstrate impressive performance in image classification tasks, driven by their strong zero-shot generalization capabilit…
Is an Ultra Large Natural Image-Based Foundation Model Superior to a Retina-Specific Model for Detecting Ocular and Systemic Diseases?
Qingshan Hou, Yukun Zhou, Jocelyn Hui Lin Goh +19
The advent of foundation models (FMs) is transforming medical domain. In ophthalmology, RETFound, a retina-specific FM pre-trained sequentially on 1.4 million natural images and 1.…
Are Traditional Deep Learning Model Approaches as Effective as a Retinal-Specific Foundation Model for Ocular and Systemic Disease Detection?
Samantha Min Er Yew, Xiaofeng Lei, Jocelyn Hui Lin Goh +26
Background: RETFound, a self-supervised, retina-specific foundation model (FM), showed potential in downstream applications. However, its comparative performance with traditional d…
Enhancing Contrastive Learning for Retinal Imaging via Adjusted Augmentation Scales
Zijie Cheng, Boxuan Li, André Altmann +2
Contrastive learning, a prominent approach within self-supervised learning, has demonstrated significant effectiveness in developing generalizable models for various applications i…
Block Expanded DINORET: Adapting Natural Domain Foundation Models for Retinal Imaging Without Catastrophic Forgetting
Jay Zoellin, Colin Merk, Mischa Buob +12
Integrating deep learning into medical imaging is poised to greatly advance diagnostic methods but it faces challenges with generalizability. Foundation models, based on self-super…
Enhancing Diagnostic Accuracy in Rare and Common Fundus Diseases with a Knowledge-Rich Vision-Language Model
Meng Wang, Tian Lin, Aidi Lin +46
Previous foundation models for fundus images were pre-trained with limited disease categories and knowledge base. Here we introduce a knowledge-rich vision-language model (RetiZero…