3 citations · 6 across the 12 of their papers we have counts for
13 papers
Can Multimodal Large Language Models Generate and Detect Multimodal Social Media Fake News?
Jiyao Yang, Yang Liu, Zhenyue Qin +2
The rapid advancement of generative AI raises concerns about the misuse of Multimodal LLMs (MLLMs) for large-scale disinformation campaigns on social media. Despite existing resear…
Are Multimodal LLMs Ready for Clinical Dermatology? A Real-World Evaluation in Dermatology
Roy Jiang, Hyunjae Kim, Zhenyue Qin +8
Multimodal large language models (MLLMs) have demonstrated promise on publicly available dermatology benchmarks. However, benchmark performance may not generalize to real-world der…
SonoSelect: Efficient Ultrasound Perception via Active Probe Exploration
Yixin Zhang, Yunzhong Hou, Longqi Li +3
Ultrasound perception typically requires multiple scan views through probe movement to reduce diagnostic ambiguity, mitigate acoustic occlusions, and improve anatomical coverage. H…
VOLMO: Versatile and Open Large Models for Ophthalmology
Zhenyue Qin, Younjoon Chung, Elijah Lee +16
Vision impairment affects millions globally, and early detection is critical to preventing irreversible vision loss. Ophthalmology workflows require clinicians to integrate medical…
Mind the Rarities: Can Rare Skin Diseases Be Reliably Diagnosed via Diagnostic Reasoning?
Yang Liu, Jiyao Yang, Hongjin Zhao +10
Large vision-language models (LVLMs) demonstrate strong performance in dermatology; however, evaluating diagnostic reasoning for rare conditions remains largely unexplored. Existin…
Beyond Medical Diagnostics: How Medical Multimodal Large Language Models Think in Space
Quoc-Huy Trinh, Xi Ding, Yang Liu +7
Visual spatial intelligence is critical for medical image interpretation, yet remains largely unexplored in Multimodal Large Language Models (MLLMs) for 3D imaging. This gap persis…
LMOD+: A Comprehensive Multimodal Dataset and Benchmark for Developing and Evaluating Multimodal Large Language Models in Ophthalmology
Zhenyue Qin, Yang Liu, Yu Yin +13
Vision-threatening eye diseases pose a major global health burden, with timely diagnosis limited by workforce shortages and restricted access to specialized care. While multimodal…
Plane Geometry Problem Solving with Multi-modal Reasoning: A Survey
Seunghyuk Cho, Zhenyue Qin, Yang Liu +3
Plane geometry problem solving (PGPS) has recently gained significant attention as a benchmark to assess the multi-modal reasoning capabilities of large vision-language models. Des…
GeoDANO: Geometric VLM with Domain Agnostic Vision Encoder
Seunghyuk Cho, Zhenyue Qin, Yang Liu +3
We introduce GeoDANO, a geometric vision-language model (VLM) with a domain-agnostic vision encoder, for solving plane geometry problems. Although VLMs have been employed for solvi…
HandCraft: Anatomically Correct Restoration of Malformed Hands in Diffusion Generated Images
Zhenyue Qin, Yiqun Zhang, Yang Liu +1
Generative text-to-image models, such as Stable Diffusion, have demonstrated a remarkable ability to generate diverse, high-quality images. However, they are surprisingly inept whe…
Visual Prompting in LLMs for Enhancing Emotion Recognition
Qixuan Zhang, Zhifeng Wang, Dylan Zhang +5
Vision Large Language Models (VLLMs) are transforming the intersection of computer vision and natural language processing. Nonetheless, the potential of using visual prompts for em…
LMOD: A Large Multimodal Ophthalmology Dataset and Benchmark for Large Vision-Language Models
Zhenyue Qin, Yu Yin, Dylan Campbell +6
The prevalence of vision-threatening eye diseases is a significant global burden, with many cases remaining undiagnosed or diagnosed too late for effective treatment. Large vision-…
Detecting and Restoring Non-Standard Hands in Stable Diffusion Generated Images
Yiqun Zhang, Zhenyue Qin, Yang Liu +1
We introduce a pipeline to address anatomical inaccuracies in Stable Diffusion generated hand images. The initial step involves constructing a specialized dataset, focusing on hand…