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Found 9 papers, 3 papers with code
VisionCLIP: An Med-AIGC based Ethical Language-Image Foundation Model for Generalizable Retina Image Analysis
Generalist foundation model has ushered in newfound capabilities in medical domain.
Leveraging Multimodal Fusion for Enhanced Diagnosis of Multiple Retinal Diseases in Ultra-wide OCTA
The construction of the M3OCTA dataset, the first multimodal OCTA dataset encompassing multiple diseases, aims to advance research in the ophthalmic image analysis community.
VisionFM: a Multi-Modal Multi-Task Vision Foundation Model for Generalist Ophthalmic Artificial Intelligence
To be commensurate with this capacity, in addition to the real data used for pre-training, we also generated and leveraged synthetic ophthalmic imaging data.
CauDR: A Causality-inspired Domain Generalization Framework for Fundus-based Diabetic Retinopathy Grading
Building on this, a causality-inspired diabetic retinopathy grading framework named CauDR was developed to eliminate spurious correlations and achieve more generalizable DR diagnostics.
Generalist Vision Foundation Models for Medical Imaging: A Case Study of Segment Anything Model on Zero-Shot Medical Segmentation
In this paper, we examine the recent Segment Anything Model (SAM) on medical images, and report both quantitative and qualitative zero-shot segmentation results on nine medical image segmentation benchmarks, covering various imaging modalities, such as optical coherence tomography (OCT), magnetic resonance imaging (MRI), and computed tomography (CT), as well as different applications including dermatology, ophthalmology, and radiology.
Large AI Models in Health Informatics: Applications, Challenges, and the Future
Large AI models, or foundation models, are models recently emerging with massive scales both parameter-wise and data-wise, the magnitudes of which can reach beyond billions.
EVEN: An Event-Based Framework for Monocular Depth Estimation at Adverse Night Conditions
Comprehensive experiments have been conducted, and the impact of different adverse weather combinations on the performance of framework has also been investigated.
MenuAI: Restaurant Food Recommendation System via a Transformer-based Deep Learning Model
The promising results, with accuracy ranging from 77. 2% to 99. 5%, have demonstrated the great potential of LTR model in addressing food recommendation problems.
Clustering Egocentric Images in Passive Dietary Monitoring with Self-Supervised Learning
By clustering images into separate events, annotators and dietitians can examine and analyze the data more efficiently and facilitate the subsequent dietary assessment processes.
