75 citations · 84 across the 10 of their papers we have counts for
11 papers
Unlocking the Power of Medical Tabular Data via Semantic-Aware Multimodal Pre-training
Yingsheng Liu, Haiming Li, Jingmin Zhu +6
While vision-language models dominate medical representation learning, unstructured text lacks the dense, quantitative diagnostic phenotypes inherent in structured clinical tables.…
M2Diff: Multi-Modality Multi-Task Enhanced Diffusion Model for MRI-Guided Low-Dose PET Enhancement
Ghulam Nabi Ahmad Hassan Yar, Himashi Peiris, Victoria Mar +2
Positron emission tomography (PET) scans expose patients to radiation, which can be mitigated by reducing the dose, albeit at the cost of diminished quality. This makes low-dose (L…
A Vision-Language Foundation Model for Zero-shot Clinical Collaboration and Automated Concept Discovery in Dermatology
Siyuan Yan, Xieji Li, Dan Mo +28
Medical foundation models have shown promise in controlled benchmarks, yet widespread deployment remains hindered by reliance on task-specific fine-tuning. Here, we introduce DermF…
Multi-Aspect Knowledge-Enhanced Medical Vision-Language Pretraining with Multi-Agent Data Generation
Xieji Li, Siyuan Yan, Yingsheng Liu +4
Vision-language pretraining (VLP) has emerged as a powerful paradigm in medical image analysis, enabling representation learning from large-scale image-text pairs without relying o…
A Multimodal Vision Foundation Model for Clinical Dermatology
Siyuan Yan, Zhen Yu, Clare Primiero +22
Diagnosing and treating skin diseases require advanced visual skills across domains and the ability to synthesize information from multiple imaging modalities. While current deep l…
Prompt-driven Latent Domain Generalization for Medical Image Classification
Siyuan Yan, Chi Liu, Zhen Yu +7
Deep learning models for medical image analysis easily suffer from distribution shifts caused by dataset artifacts bias, camera variations, differences in the imaging station, etc.…
Revamping AI Models in Dermatology: Overcoming Critical Challenges for Enhanced Skin Lesion Diagnosis
Deval Mehta, Brigid Betz-Stablein, Toan D Nguyen +8
The surge in developing deep learning models for diagnosing skin lesions through image analysis is notable, yet their clinical black faces challenges. Current dermatology AI models…
EPVT: Environment-aware Prompt Vision Transformer for Domain Generalization in Skin Lesion Recognition
Siyuan Yan, Chi Liu, Zhen Yu +6
Skin lesion recognition using deep learning has made remarkable progress, and there is an increasing need for deploying these systems in real-world scenarios. However, recent resea…
Skin Lesion Recognition with Class-Hierarchy Regularized Hyperbolic Embeddings
Zhen Yu, Toan Nguyen, Yaniv Gal +7
In practice, many medical datasets have an underlying taxonomy defined over the disease label space. However, existing classification algorithms for medical diagnoses often assume…
Early Melanoma Diagnosis with Sequential Dermoscopic Images
Zhen Yu, Jennifer Nguyen, Toan D Nguyen +6
Dermatologists often diagnose or rule out early melanoma by evaluating the follow-up dermoscopic images of skin lesions. However, existing algorithms for early melanoma diagnosis a…
Melanoma Diagnosis with Spatio-Temporal Feature Learning on Sequential Dermoscopic Images
Zhen Yu, Jennifer Nguyen, Xiaojun Chang +5
Existing studies for automated melanoma diagnosis are based on single-time point images of lesions. However, melanocytic lesions de facto are progressively evolving and, moreover,…