30 citations · 114 across the 30 of their papers we have counts for
19 papers · 1 filter
Unified Multimodal Model for Brain MRI Imputation and Understanding
Zhiyun Song, Che Liu, Tian Xia +2
Multimodal large language models (MLLMs) hold great potential for medicine, as they inherit knowledge from LLM and allow multiple data modalities to be integrated, analysed and int…
MedOpenClaw and MedFlowBench: Auditing Medical Agents in Full-Study Workflows
Weixiang Shen, Chengzhi Shen, Yanzhu Hu +12
Medical imaging benchmarks often evaluate VLMs on pre-selected 2D images, slices, crops, or patches, making evaluation closer to visual recognition. Real clinical workflows impose…
M3Ret: Unleashing Zero-shot Multimodal Medical Image Retrieval via Self-Supervision
Che Liu, Zheng Jiang, Chengyu Fang +5
Medical image retrieval is essential for clinical decision-making and translational research, relying on discriminative visual representations. Yet, current methods remain fragment…
Knowledge to Sight: Reasoning over Visual Attributes via Knowledge Decomposition for Abnormality Grounding
Jun Li, Che Liu, Wenjia Bai +4
In this work, we address the problem of grounding abnormalities in medical images, where the goal is to localize clinical findings based on textual descriptions. While generalist V…
How Far Have Medical Vision-Language Models Come? A Comprehensive Benchmarking Study
Che Liu, Jiazhen Pan, Weixiang Shen +3
Vision-Language Models (VLMs) trained on web-scale corpora excel at natural image tasks and are increasingly repurposed for healthcare; however, their competence in medical tasks r…
BOTM: Echocardiography Segmentation via Bi-directional Optimal Token Matching
Zhihua Liu, Lei Tong, Xilin He +4
Existed echocardiography segmentation methods often suffer from anatomical inconsistency challenge caused by shape variation, partial observation and region ambiguity with similar…
Enhancing Abnormality Grounding for Vision Language Models with Knowledge Descriptions
Jun Li, Che Liu, Wenjia Bai +3
Visual Language Models (VLMs) have demonstrated impressive capabilities in visual grounding tasks. However, their effectiveness in the medical domain, particularly for abnormality…
How Does Diverse Interpretability of Textual Prompts Impact Medical Vision-Language Zero-Shot Tasks?
Sicheng Wang, Che Liu, Rossella Arcucci
Recent advancements in medical vision-language pre-training (MedVLP) have significantly enhanced zero-shot medical vision tasks such as image classification by leveraging large-sca…
Can Medical Vision-Language Pre-training Succeed with Purely Synthetic Data?
Che Liu, Zhongwei Wan, Haozhe Wang +6
Medical Vision-Language Pre-training (MedVLP) has made significant progress in enabling zero-shot tasks for medical image understanding. However, training MedVLP models typically r…
FMBench: Benchmarking Fairness in Multimodal Large Language Models on Medical Tasks
Peiran Wu, Che Liu, Canyu Chen +3
Advancements in Multimodal Large Language Models (MLLMs) have significantly improved medical task performance, such as Visual Question Answering (VQA) and Report Generation (RG). H…
Argus: Benchmarking and Enhancing Vision-Language Models for 3D Radiology Report Generation
Che Liu, Zhongwei Wan, Yuqi Wang +5
Automatic radiology report generation holds significant potential to streamline the labor-intensive process of report writing by radiologists, particularly for 3D radiographs such…
BIMCV-R: A Landmark Dataset for 3D CT Text-Image Retrieval
Yinda Chen, Che Liu, Xiaoyu Liu +2
The burgeoning integration of 3D medical imaging into healthcare has led to a substantial increase in the workload of medical professionals. To assist clinicians in their diagnosti…
Freeze the backbones: A Parameter-Efficient Contrastive Approach to Robust Medical Vision-Language Pre-training
Jiuming Qin, Che Liu, Sibo Cheng +2
Modern healthcare often utilises radiographic images alongside textual reports for diagnostics, encouraging the use of Vision-Language Self-Supervised Learning (VL-SSL) with large…
T3D: Advancing 3D Medical Vision-Language Pre-training by Learning Multi-View Visual Consistency
Che Liu, Cheng Ouyang, Yinda Chen +7
While 3D visual self-supervised learning (vSSL) shows promising results in capturing visual representations, it overlooks the clinical knowledge from radiology reports. Meanwhile,…
G2D: From Global to Dense Radiography Representation Learning via Vision-Language Pre-training
Che Liu, Cheng Ouyang, Sibo Cheng +3
Recently, medical vision-language pre-training (VLP) has reached substantial progress to learn global visual representation from medical images and their paired radiology reports.…
IMITATE: Clinical Prior Guided Hierarchical Vision-Language Pre-training
Che Liu, Sibo Cheng, Miaojing Shi +3
In the field of medical Vision-Language Pre-training (VLP), significant efforts have been devoted to deriving text and image features from both clinical reports and associated medi…
Utilizing Synthetic Data for Medical Vision-Language Pre-training: Bypassing the Need for Real Images
Che Liu, Anand Shah, Wenjia Bai +1
Medical Vision-Language Pre-training (VLP) learns representations jointly from medical images and paired radiology reports. It typically requires large-scale paired image-text data…
M-FLAG: Medical Vision-Language Pre-training with Frozen Language Models and Latent Space Geometry Optimization
Che Liu, Sibo Cheng, Chen Chen +5
Medical vision-language models enable co-learning and integrating features from medical imaging and clinical text. However, these models are not easy to train and the latent repres…
Generative Text-Guided 3D Vision-Language Pretraining for Unified Medical Image Segmentation
Yinda Chen, Che Liu, Wei Huang +3
Vision-Language Pretraining (VLP) has demonstrated remarkable capabilities in learning visual representations from textual descriptions of images without annotations. Yet, effectiv…