48 citations · 109 across the 5 of their papers we have counts for
5 papers
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…
MIS-FM: 3D Medical Image Segmentation using Foundation Models Pretrained on a Large-Scale Unannotated Dataset
Guotai Wang, Jianghao Wu, Xiangde Luo +3
Pretraining with large-scale 3D volumes has a potential for improving the segmentation performance on a target medical image dataset where the training images and annotations are l…
One-shot Weakly-Supervised Segmentation in Medical Images
Wenhui Lei, Qi Su, Ran Gu +5
Deep neural networks usually require accurate and a large number of annotations to achieve outstanding performance in medical image segmentation. One-shot segmentation and weakly-s…
Pulmonary Vessel Segmentation based on Orthogonal Fused U-Net++ of Chest CT Images
Hejie Cui, Xinglong Liu, Ning Huang
Pulmonary vessel segmentation is important for clinical diagnosis of pulmonary diseases, while is also challenging due to the complicated structure. In this work, we present an eff…
SenseCare: A Research Platform for Medical Image Informatics and Interactive 3D Visualization
Qi Duan, Guotai Wang, Rui Wang +12
Clinical research on smart health has an increasing demand for intelligent and clinic-oriented medical image computing algorithms and platforms that support various applications. T…