60 citations · 268 across the 22 of their papers we have counts for
22 papers
A Vision-language Framework for Comparative Reasoning in Radiology
Tengfei Zhang, Ziheng Zhao, Xiaoman Zhang +5
Medical imaging artificial intelligence has achieved strong performance in isolated image interpretation, but remains poorly aligned with radiological practice, where diagnosis and…
Boosting Pathology Foundation Models via Few-shot Prompt-tuning for Rare Cancer Subtyping
Dexuan He, Xiao Zhou, Wenbin Guan +11
Rare cancers comprise 20-25% of all malignancies but face major diagnostic challenges due to limited expert availability-especially in pediatric oncology, where they represent over…
An Agentic System for Rare Disease Diagnosis with Traceable Reasoning
Weike Zhao, Chaoyi Wu, Yanjie Fan +10
Rare diseases affect over 300 million individuals worldwide, yet timely and accurate diagnosis remains an urgent challenge. Patients often endure a prolonged diagnostic odyssey exc…
AutoRG-Brain: Grounded Report Generation for Brain MRI
Jiayu Lei, Xiaoman Zhang, Chaoyi Wu +6
Radiologists are tasked with interpreting a large number of images in a daily base, with the responsibility of generating corresponding reports. This demanding workload elevates th…
RadGenome-Chest CT: A Grounded Vision-Language Dataset for Chest CT Analysis
Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao +4
Developing generalist foundation model has recently attracted tremendous attention among researchers in the field of AI for Medicine (AI4Medicine). A pivotal insight in developing…
Knowledge-enhanced Visual-Language Pretraining for Computational Pathology
Xiao Zhou, Xiaoman Zhang, Chaoyi Wu +3
In this paper, we consider the problem of visual representation learning for computational pathology, by exploiting large-scale image-text pairs gathered from public resources, alo…
Towards Building Multilingual Language Model for Medicine
Pengcheng Qiu, Chaoyi Wu, Xiaoman Zhang +5
The development of open-source, multilingual medical language models can benefit a wide, linguistically diverse audience from different regions. To promote this domain, we present…
Large-Vocabulary Segmentation for Medical Images with Text Prompts
Ziheng Zhao, Yao Zhang, Chaoyi Wu +5
This paper aims to build a model that can Segment Anything in 3D medical images, driven by medical terminologies as Text prompts, termed as SAT. Our main contributions are three-fo…
Large-scale Long-tailed Disease Diagnosis on Radiology Images
Qiaoyu Zheng, Weike Zhao, Chaoyi Wu +7
Developing a generalist radiology diagnosis system can greatly enhance clinical diagnostics. In this paper, we introduce RadDiag, a foundational model supporting 2D and 3D inputs a…
Can GPT-4V(ision) Serve Medical Applications? Case Studies on GPT-4V for Multimodal Medical Diagnosis
Chaoyi Wu, Jiayu Lei, Qiaoyu Zheng +8
Driven by the large foundation models, the development of artificial intelligence has witnessed tremendous progress lately, leading to a surge of general interest from the public.…
UniBrain: Universal Brain MRI Diagnosis with Hierarchical Knowledge-enhanced Pre-training
Jiayu Lei, Lisong Dai, Haoyun Jiang +9
Magnetic resonance imaging~(MRI) have played a crucial role in brain disease diagnosis, with which a range of computer-aided artificial intelligence methods have been proposed. How…
Towards Generalist Foundation Model for Radiology by Leveraging Web-scale 2D&3D Medical Data
Chaoyi Wu, Xiaoman Zhang, Ya Zhang +2
In this study, we aim to initiate the development of Radiology Foundation Model, termed as RadFM. We consider the construction of foundational models from three perspectives, namel…
PMC-VQA: Visual Instruction Tuning for Medical Visual Question Answering
Xiaoman Zhang, Chaoyi Wu, Ziheng Zhao +4
Medical Visual Question Answering (MedVQA) presents a significant opportunity to enhance diagnostic accuracy and healthcare delivery by leveraging artificial intelligence to interp…
PMC-LLaMA: Towards Building Open-source Language Models for Medicine
Chaoyi Wu, Weixiong Lin, Xiaoman Zhang +3
Recently, Large Language Models (LLMs) have showcased remarkable capabilities in natural language understanding. While demonstrating proficiency in everyday conversations and quest…
PMC-CLIP: Contrastive Language-Image Pre-training using Biomedical Documents
Weixiong Lin, Ziheng Zhao, Xiaoman Zhang +4
Foundation models trained on large-scale dataset gain a recent surge in CV and NLP. In contrast, development in biomedical domain lags far behind due to data scarcity. To address t…
K-Diag: Knowledge-enhanced Disease Diagnosis in Radiographic Imaging
Chaoyi Wu, Xiaoman Zhang, Yanfeng Wang +2
In this paper, we consider the problem of disease diagnosis. Unlike the conventional learning paradigm that treats labels independently, we propose a knowledge-enhanced framework,…
Knowledge-enhanced Visual-Language Pre-training on Chest Radiology Images
Xiaoman Zhang, Chaoyi Wu, Ya Zhang +2
While multi-modal foundation models pre-trained on large-scale data have been successful in natural language understanding and vision recognition, their use in medical domains is s…
MedKLIP: Medical Knowledge Enhanced Language-Image Pre-Training in Radiology
Chaoyi Wu, Xiaoman Zhang, Ya Zhang +2
In this paper, we consider enhancing medical visual-language pre-training (VLP) with domain-specific knowledge, by exploiting the paired image-text reports from the radiological da…
Self-supervised Tumor Segmentation through Layer Decomposition
Xiaoman Zhang, Weidi Xie, Chaoqin Huang +4
In this paper, we target self-supervised representation learning for zero-shot tumor segmentation. We make the following contributions: First, we advocate a zero-shot setting, wher…
MS-KD: Multi-Organ Segmentation with Multiple Binary-Labeled Datasets
Shixiang Feng, Yuhang Zhou, Xiaoman Zhang +2
Annotating multiple organs in 3D medical images is time-consuming and costly. Meanwhile, there exist many single-organ datasets with one specific organ annotated. This paper invest…
Uncertainty-aware Incremental Learning for Multi-organ Segmentation
Yuhang Zhou, Xiaoman Zhang, Shixiang Feng +2
Most existing approaches to train a unified multi-organ segmentation model from several single-organ datasets require simultaneously access multiple datasets during training. In th…
SAR: Scale-Aware Restoration Learning for 3D Tumor Segmentation
Xiaoman Zhang, Shixiang Feng, Yuhang Zhou +2
Automatic and accurate tumor segmentation on medical images is in high demand to assist physicians with diagnosis and treatment. However, it is difficult to obtain massive amounts…