36 citations · 40 across the 6 of their papers we have counts for
6 papers
Foundation Model-guided Iteratively Prompting and Pseudo-Labeling for Partially Labeled Medical Image Segmentation
Qiaochu Zhao, Wei Wei, David Horowitz +2
Automated medical image segmentation has achieved remarkable progress with fully labeled data. However, site-specific clinical priorities and the high cost of manual annotation oft…
Multi-View Attention Learning for Residual Disease Prediction of Ovarian Cancer
Xiangneng Gao, Shulan Ruan, Jun Shi +2
In the treatment of ovarian cancer, precise residual disease prediction is significant for clinical and surgical decision-making. However, traditional methods are either invasive (…
DARNet: Dual-Attention Residual Network for Automatic Diagnosis of COVID-19 via CT Images
Jun Shi, Huite Yi, Shulan Ruan +4
The ongoing global pandemic of Coronavirus Disease 2019 (COVID-19) poses a serious threat to public health and the economy. Rapid and accurate diagnosis of COVID-19 is crucial to p…
Understanding Heart-Failure Patients EHR Clinical Features via SHAP Interpretation of Tree-Based Machine Learning Model Predictions
Shuyu Lu, Ruoyu Chen, Wei Wei +1
Heart failure (HF) is a major cause of mortality. Accurately monitoring HF progress and adjust therapies are critical for improving patient outcomes. An experienced cardiologist ca…
Dynamic Graph Correlation Learning for Disease Diagnosis with Incomplete Labels
Daizong Liu, Shuangjie Xu, Pan Zhou +3
Disease diagnosis on chest X-ray images is a challenging multi-label classification task. Previous works generally classify the diseases independently on the input image without co…
Thoracic Disease Identification and Localization with Limited Supervision
Zhe Li, Chong Wang, Mei Han +4
Accurate identification and localization of abnormalities from radiology images play an integral part in clinical diagnosis and treatment planning. Building a highly accurate predi…