2 citations · 4 across the 10 of their papers we have counts for
8 papers · 1 filter
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…
MADCrowner: Margin Aware Dental Crown Design with Template Deformation and Refinement
Linda Wei, Chang Liu, Wenran Zhang +9
Dental crown restoration is one of the most common treatment modalities for tooth defect, where personalized dental crown design is critical. While computer-aided design (CAD) syst…
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…
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…
GiT: Towards Generalist Vision Transformer through Universal Language Interface
Haiyang Wang, Hao Tang, Li Jiang +5
This paper proposes a simple, yet effective framework, called GiT, simultaneously applicable for various vision tasks only with a vanilla ViT. Motivated by the universality of the…
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…