7 citations · 10 across the 10 of their papers we have counts for
11 papers
FOCUS: Benchmarking Retinal Model Generalization from Foundation Vision Encoders to Multimodal LLMs
David Restrepo, Chenwei Wu, Luis Filipe Nakayama +4
Progress in AI-based retinal image analysis has accelerated with the adoption of foundation models, yet evaluating their real-world reliability remains challenging. Performance rep…
On the Cone Effect and Modality Gap in Medical Vision-Language Embeddings
David Restrepo, Miguel L Martins, Chenwei Wu +5
Vision-Language Models (VLMs) exhibit a characteristic "cone effect" in which nonlinear encoders map embeddings into highly concentrated regions of the representation space, contri…
HGP-Mamba: Integrating Histology and Generated Protein Features for Mamba-based Multimodal Survival Risk Prediction
Jing Dai, Chen Wu, Ming Wu +4
Recent advances in multimodal learning have significantly improved cancer survival risk prediction. However, the joint prognostic potential of protein markers and histopathology im…
CURENet: Combining Unified Representations for Efficient Chronic Disease Prediction
Cong-Tinh Dao, Nguyen Minh Thao Phan, Jun-En Ding +12
Electronic health records (EHRs) are designed to synthesize diverse data types, including unstructured clinical notes, structured lab tests, and time-series visit data. Physicians…
Representation Learning of Lab Values via Masked AutoEncoders
David Restrepo, Chenwei Wu, Yueran Jia +5
Accurate imputation of missing laboratory values in electronic health records (EHRs) is critical to enable robust clinical predictions and reduce biases in AI systems in healthcare…
Multi-OphthaLingua: A Multilingual Benchmark for Assessing and Debiasing LLM Ophthalmological QA in LMICs
David Restrepo, Chenwei Wu, Zhengxu Tang +14
Current ophthalmology clinical workflows are plagued by over-referrals, long waits, and complex and heterogeneous medical records. Large language models (LLMs) present a promising…
MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models
Thao Minh Nguyen Phan, Cong-Tinh Dao, Chenwei Wu +7
Electronic health records (EHRs) are multimodal by nature, consisting of structured tabular features like lab tests and unstructured clinical notes. In real-life clinical practice,…
Efficient In-Context Medical Segmentation with Meta-driven Visual Prompt Selection
Chenwei Wu, David Restrepo, Zitao Shuai +2
In-context learning (ICL) with Large Vision Models (LVMs) presents a promising avenue in medical image segmentation by reducing the reliance on extensive labeling. However, the ICL…
Multimodal Deep Learning for Low-Resource Settings: A Vector Embedding Alignment Approach for Healthcare Applications
David Restrepo, Chenwei Wu, Sebastián Andrés Cajas +3
Large-scale multi-modal deep learning models have revolutionized domains such as healthcare, highlighting the importance of computational power. However, in resource-constrained re…
DF-DM: A foundational process model for multimodal data fusion in the artificial intelligence era
David Restrepo, Chenwei Wu, Constanza Vásquez-Venegas +3
In the big data era, integrating diverse data modalities poses significant challenges, particularly in complex fields like healthcare. This paper introduces a new process model for…
Analyzing Diversity in Healthcare LLM Research: A Scientometric Perspective
David Restrepo, Chenwei Wu, Constanza Vásquez-Venegas +5
The deployment of large language models (LLMs) in healthcare has demonstrated substantial potential for enhancing clinical decision-making, administrative efficiency, and patient o…