2 citations · 6 across the 17 of their papers we have counts for
9 papers · 1 filter
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…
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…
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…
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…
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…