4 citations · 11 across the 11 of their papers we have counts for
11 papers
A unified multi-task framework enables interpretable chest radiograph analysis
Lijian Xu, Ziyu Ni, Xinglong Liu +3
While multimodal deep learning has advanced medical imaging analysis, existing black-box systems \textcolor{black}{may remain confined to isolated tasks, often overlooking} the tru…
VBCD: A Voxel-Based Framework for Personalized Dental Crown Design
Linda Wei, Chang Liu, Wenran Zhang +3
The design of restorative dental crowns from intraoral scans is labor-intensive for dental technicians. To address this challenge, we propose a novel voxel-based framework for auto…
One Leaf Reveals the Season: Occlusion-Based Contrastive Learning with Semantic-Aware Views for Efficient Visual Representation
Xiaoyu Yang, Lijian Xu, Hongsheng Li +1
This paper proposes a scalable and straightforward pre-training paradigm for efficient visual conceptual representation called occluded image contrastive learning (OCL). Our OCL ap…
A foundation model for generalizable disease diagnosis in chest X-ray images
Lijian Xu, Ziyu Ni, Hao Sun +2
Medical artificial intelligence (AI) is revolutionizing the interpretation of chest X-ray (CXR) images by providing robust tools for disease diagnosis. However, the effectiveness o…
MedViLaM: A multimodal large language model with advanced generalizability and explainability for medical data understanding and generation
Lijian Xu, Hao Sun, Ziyu Ni +2
Medicine is inherently multimodal and multitask, with diverse data modalities spanning text, imaging. However, most models in medical field are unimodal single tasks and lack good…
Multi-modal Data Binding for Survival Analysis Modeling with Incomplete Data and Annotations
Linhao Qu, Dan Huang, Shaoting Zhang +1
Survival analysis stands as a pivotal process in cancer treatment research, crucial for predicting patient survival rates accurately. Recent advancements in data collection techniq…
Pathology-knowledge Enhanced Multi-instance Prompt Learning for Few-shot Whole Slide Image Classification
Linhao Qu, Dingkang Yang, Dan Huang +4
Current multi-instance learning algorithms for pathology image analysis often require a substantial number of Whole Slide Images for effective training but exhibit suboptimal perfo…
Cost-effective Instruction Learning for Pathology Vision and Language Analysis
Kaitao Chen, Mianxin Liu, Fang Yan +8
The advent of vision-language models fosters the interactive conversations between AI-enabled models and humans. Yet applying these models into clinics must deal with daunting chal…
Enhancing Visual Grounding and Generalization: A Multi-Task Cycle Training Approach for Vision-Language Models
Xiaoyu Yang, Lijian Xu, Hao Sun +2
Visual grounding (VG) occupies a pivotal position in multi-modality vision-language models. In this study, we propose ViLaM, a large multi-modality model, that supports multi-tasks…
Learning A Multi-Task Transformer Via Unified And Customized Instruction Tuning For Chest Radiograph Interpretation
Lijian Xu, Ziyu Ni, Xinglong Liu +3
The emergence of multi-modal deep learning models has made significant impacts on clinical applications in the last decade. However, the majority of models are limited to single-ta…
Boosting Dermatoscopic Lesion Segmentation via Diffusion Models with Visual and Textual Prompts
Shiyi Du, Xiaosong Wang, Yongyi Lu +5
Image synthesis approaches, e.g., generative adversarial networks, have been popular as a form of data augmentation in medical image analysis tasks. It is primarily beneficial to o…