activity
20232026
most citedPrompt-driven Latent Domain Generalization for Medical Image Classification

2 citations · 5 across the 6 of their papers we have counts for

collaborators

7 papers

cs.CV2026

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.…

cs.CV2026

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…

cs.CV2025

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…

cs.HC2025★ 1 cited

Supporting Data-Frame Dynamics in AI-assisted Decision Making

Chengbo Zheng, Tim Miller, Alina Bialkowski +2

High stakes decision-making often requires a continuous interplay between evolving evidence and shifting hypotheses, a dynamic that is not well supported by current AI decision sup…

cs.CV2024★ 1 cited

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…

eess.IV2024★ 2 cited

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.…

eess.IV2023★ 1 cited

Application of Machine Learning in Melanoma Detection and the Identification of 'Ugly Duckling' and Suspicious Naevi: A Review

Fatima Al Zegair, Nathasha Naranpanawa, Brigid Betz-Stablein +3

Skin lesions known as naevi exhibit diverse characteristics such as size, shape, and colouration. The concept of an "Ugly Duckling Naevus" comes into play when monitoring for melan…