1k citations
- ShanghaiTech UniversityCN11 papers
- Shanghai Clinical Research CenterCN7 papers
- Shanghai Jiao Tong UniversityCN7 papers
- Affiliated Hangzhou First People's Hospital, Westlake University, School of MedicineCN3 papers
- Central South UniversityCN3 papers
- Fudan UniversityCN3 papers
- Nanjing UniversityCN3 papers
- Second Xiangya Hospital of Central South UniversityCN3 papers
- Southeast UniversityCN3 papers
- United Imaging Intelligence (China)CN3 papers
- Xi'an Jiaotong UniversityCN3 papers
- Zhejiang UniversityCN3 papers
20 papers
Robust probabilistic measurement of structural-functional module consistency in infant brain development
Lingbin Bian, Feihong Liu, Qian Wang +3
Brain network is commonly divided into modules for analyzing their functionally segregated roles for group-level analysis in neuroimaging studies. Here, we introduce stochastic mod…
PET Head Motion Estimation Using Supervised Deep Learning with Attention
Zhuotong Cai, Tianyi Zeng, Jiazhen Zhang +8
Head movement poses a significant challenge in brain positron emission tomography (PET) imaging, resulting in image artifacts and tracer uptake quantification inaccuracies. Effecti…
Patch Progression Masked Autoencoder with Fusion CNN Network for Classifying Evolution Between Two Pairs of 2D OCT Slices
Philippe Zhang, Weili Jiang, Yihao Li +13
Age-related Macular Degeneration (AMD) is a prevalent eye condition affecting visual acuity. Anti-vascular endothelial growth factor (anti-VEGF) treatments have been effective in s…
Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge
Yang Nan, Xiaodan Xing, Shiyi Wang +38
Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains…
ChatCAD+: Towards a Universal and Reliable Interactive CAD using LLMs
Zihao Zhao, Sheng Wang, Jinchen Gu +6
The integration of Computer-Aided Diagnosis (CAD) with Large Language Models (LLMs) presents a promising frontier in clinical applications, notably in automating diagnostic process…
AdaMSS: Adaptive Multi-Modality Segmentation-to-Survival Learning for Survival Outcome Prediction from PET/CT Images
Mingyuan Meng, Bingxin Gu, Michael Fulham +4
Survival prediction is a major concern for cancer management. Deep survival models based on deep learning have been widely adopted to perform end-to-end survival prediction from me…