A5101576448- Radiomics and Machine Learning in Medical Imaging
- AI in cancer detection
- Gastric Cancer Management and Outcomes
- Gastrointestinal Tumor Research and Treatment
- Rough Sets and Fuzzy Logic
- Cervical Cancer and HPV Research
- Advanced Clustering Algorithms Research
- Colorectal Cancer Screening and Detection
- Adenosine and Purinergic Signaling
- Head and Neck Cancer Studies
- Autophagy in Disease and Therapy
- Privacy-Preserving Technologies in Data
- Endoplasmic Reticulum Stress and Disease
- Data Management and Algorithms
- Brain Tumor Detection and Classification
- Artificial Intelligence in Healthcare and Education
- Medical Imaging and Analysis
Chinese University of Hong Kong
2025
Shandong Institute of Automation
2023-2024
Chinese Academy of Sciences
2023-2024
Fujian Medical University
2024
University of Chinese Academy of Sciences
2014-2023
Beijing Academy of Artificial Intelligence
2023
Jiujiang University
2019
Abstract Federated learning (FL) has shown great potential in addressing data privacy issues medical image analysis. However, varying distributions across different sites can create challenges aggregating client models and achieving good global model performance. In this study, we propose a novel personalized contrastive representation FL framework, named PCRFed, which leverages to address the non-independent identically distributed (non-IID) challenge dynamically adjusts distance between...
Endoplasmic reticulum (ER) stress and autophagy are involved in myocardial ischemia‑reperfusion (I/R) injury; however, their roles this type of injury remain unclear. The present study investigated the ER autophagy, underlying mechanisms, H9c2 cells during hypoxia/reoxygenation (H/R) injury. Cell viability was detected by CCK‑8 assay. flux monitored with mCherry‑GFP‑LC3‑adenovirus transfection. expression levels autophagy‑related proteins stress‑related were measured western blotting....
The potential prognostic value of extranodal soft tissue metastasis (ESTM) has been confirmed by increasing studies about gastric cancer (GC). However, the gold standard ESTM is determined pathologic examination after surgery, and there are no preoperative methods for assessment yet.
Prognostic assessment remains a critical challenge in medical research, often limited by the lack of well-labeled data. In this work, we introduce ContraSurv, weakly-supervised learning framework based on contrastive learning, designed to enhance prognostic predictions 3D images. ContraSurv utilizes both self-supervised information inherent unlabeled data and cues present censored data, refining its capacity extract representations. For purpose, establish Vision Transformer architecture...
Tumor localization and lymph node metastasis (LNM) diagnosis are two important tasks for gynecologist to make decisions in cervical cancer treatments. Aiming develop an accurate convenient system, we propose a multi-task residual cross-attention network named MRCNet tumor segmentation LNM prediction. Specifically, tackle task correlation with underlying related supervision information, capture multi-level features by multi-scale convolutional neural network, which equipped module concerning...
This study aimed to compare the efficacy of Single-Port plus One Port Laparoscopy (SILS + 1) with Conventional Multi-Port Laparoscopic Surgery (CLS) and investigate risk factors associated postoperative complications in patients sigmoid colon cancer undergoing laparoscopy. Male treated at our hospital were selected evenly distributed into CLS SILS 1 groups further categorized complication non-complication based on their status within 30 days post-surgery. Compared group, SILS+ group had...