Area of research
Computer Vision and Pattern Recognition · Biomedical Engineering
Research interest
Research interests include Artificial intelligence, Segmentation, Computer science, Random forest, Receiver operating characteristic, and Coronavirus disease 2019 (COVID-19).
Automated segmentation of brain metastases with deep learning: A multi-center, randomized crossover, multi-reader evaluation study
Development and validation of a deep-learning model for detecting brain metastases on 3D post-contrast MRI: a multi-center multi-reader evaluation study
A deep learning-based quantitative computed tomography model for predicting the severity of COVID-19: a retrospective study of 196 patients
Dual-Sampling Attention Network for Diagnosis of COVID-19 From Community Acquired Pneumonia
The state of the art in kidney and kidney tumor segmentation in contrast-enhanced CT imaging: Results of the KiTS19 challenge
Guest Editorial: Special Issue on Imaging-Based Diagnosis of COVID-19
Hippocampal Segmentation From Longitudinal Infant Brain MR Images via Classification-Guided Boundary Regression
Accurate Segmentation of CT Male Pelvic Organs via Regression-Based Deformable Models and Multi-Task Random Forests
Concatenated spatially-localized random forests for hippocampus labeling in adult and infant MR brain images
In vivo MRI based prostate cancer localization with random forests and auto-context model
Automated segmentation of dental CBCT image with prior-guided sequential random forests
Locally-constrained boundary regression for segmentation of prostate and rectum in the planning CT images
Automated bone segmentation from dental CBCT images using patch‐based sparse representation and convex optimization
Hierarchical Lung Field Segmentation With Joint Shape and Appearance Sparse Learning
Representation Learning: A Unified Deep Learning Framework for Automatic Prostate MR Segmentation