most citedBenchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook

4 citations · 6 across the 4 of their papers we have counts for

collaborators

5 papers

q-bio.QM2026

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…

cs.LG2026

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…

cs.CV2025

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…

cs.CV2025★ 4 cited

Benchmarking Real-World Medical Image Classification with Noisy Labels: Challenges, Practice, and Outlook

Yuan Ma, Junlin Hou, Chao Zhang +4

Learning from noisy labels remains a major challenge in medical image analysis, where annotation demands expert knowledge and substantial inter-observer variability often leads to…

eess.IV2025★ 2 cited

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…