118 citations · 399 across the 25 of their papers we have counts for
14 papers · 1 filter
Holistic Evaluation of GPT-4V for Biomedical Imaging
Zhengliang Liu, Hanqi Jiang, Tianyang Zhong +47
In this paper, we present a large-scale evaluation probing GPT-4V's capabilities and limitations for biomedical image analysis. GPT-4V represents a breakthrough in artificial gener…
PolicyGPT: Automated Analysis of Privacy Policies with Large Language Models
Chenhao Tang, Zhengliang Liu, Chong Ma +8
Privacy policies serve as the primary conduit through which online service providers inform users about their data collection and usage procedures. However, in a bid to be comprehe…
Chat2Brain: A Method for Mapping Open-Ended Semantic Queries to Brain Activation Maps
Yaonai Wei, Tuo Zhang, Han Zhang +10
Over decades, neuroscience has accumulated a wealth of research results in the text modality that can be used to explore cognitive processes. Meta-analysis is a typical method that…
Radiology-Llama2: Best-in-Class Large Language Model for Radiology
Zhengliang Liu, Yiwei Li, Peng Shu +18
This paper introduces Radiology-Llama2, a large language model specialized for radiology through a process known as instruction tuning. Radiology-Llama2 is based on the Llama2 arch…
Review of Large Vision Models and Visual Prompt Engineering
Jiaqi Wang, Zhengliang Liu, Lin Zhao +18
Visual prompt engineering is a fundamental technology in the field of visual and image Artificial General Intelligence, serving as a key component for achieving zero-shot capabilit…
Evaluating Large Language Models for Radiology Natural Language Processing
Zhengliang Liu, Tianyang Zhong, Yiwei Li +43
The rise of large language models (LLMs) has marked a pivotal shift in the field of natural language processing (NLP). LLMs have revolutionized a multitude of domains, and they hav…
SAMAug: Point Prompt Augmentation for Segment Anything Model
Haixing Dai, Chong Ma, Zhiling Yan +14
This paper introduces SAMAug, a novel visual point augmentation method for the Segment Anything Model (SAM) that enhances interactive image segmentation performance. SAMAug generat…
Radiology-GPT: A Large Language Model for Radiology
Zhengliang Liu, Aoxiao Zhong, Yiwei Li +17
We introduce Radiology-GPT, a large language model for radiology. Using an instruction tuning approach on an extensive dataset of radiology domain knowledge, Radiology-GPT demonstr…
Learning Better Contrastive View from Radiologist's Gaze
Sheng Wang, Zixu Zhuang, Xi Ouyang +6
Recent self-supervised contrastive learning methods greatly benefit from the Siamese structure that aims to minimizing distances between positive pairs. These methods usually apply…
Instruction-ViT: Multi-Modal Prompts for Instruction Learning in ViT
Zhenxiang Xiao, Yuzhong Chen, Lu Zhang +14
Prompts have been proven to play a crucial role in large language models, and in recent years, vision models have also been using prompts to improve scalability for multiple downst…
ChatABL: Abductive Learning via Natural Language Interaction with ChatGPT
Tianyang Zhong, Yaonai Wei, Li Yang +13
Large language models (LLMs) such as ChatGPT have recently demonstrated significant potential in mathematical abilities, providing valuable reasoning paradigm consistent with human…
Exploring the Trade-Offs: Unified Large Language Models vs Local Fine-Tuned Models for Highly-Specific Radiology NLI Task
Zihao Wu, Lu Zhang, Chao Cao +12
Recently, ChatGPT and GPT-4 have emerged and gained immense global attention due to their unparalleled performance in language processing. Despite demonstrating impressive capabili…
Prompt Engineering for Healthcare: Methodologies and Applications
Jiaqi Wang, Enze Shi, Sigang Yu +21
Prompt engineering is a critical technique in the field of natural language processing that involves designing and optimizing the prompts used to input information into models, aim…
An Iterative Optimizing Framework for Radiology Report Summarization with ChatGPT
Chong Ma, Zihao Wu, Jiaqi Wang +13
The 'Impression' section of a radiology report is a critical basis for communication between radiologists and other physicians, and it is typically written by radiologists based on…