Area of research
Radiation · Radiology, Nuclear Medicine and Imaging
Research interest
Research interests include Medicine, Nuclear medicine, Artificial intelligence, Deep learning, Computer science, and Radiation therapy.
3D transformer-based dose prediction in HDR brachytherapy for cervical cancer
Dosimetric optimization and clinical feasibility of the block technique in helical tomotherapy for bilateral breast cancer
Megavoltage CT enhancement for cervical cancer tomotherapy using a generative adversarial network with deformable convolution and self‐attention
Deep learning with attention modules and residual transformations improves hepatocellular carcinoma (HCC) differentiation using multiphase CT
A deep learning model for predicting radiation-induced xerostomia in patients with head and neck cancer based on multi-channel fusion
A hierarchical multi-channel multi-modality fusion strategy for integrating digital mammography and synthesized digital breast tomosynthesis for breast mass discrimination
Radiation induced liver injury (RILI) evaluation using longitudinal computed tomography (CT) in image‐guided precision murine radiotherapy
3D transformer-based dose prediction in HDR brachytherapy for cervical cancer
Attention-Gated Deep-Learning–Based Automatic Digitization of Interstitial Needles in High-Dose-Rate Brachytherapy for Cervical Cancer
Synthetic CT generation from cone-beam CT using deep-learning for breast adaptive radiotherapy
Automatic prediction model for online diaphragm motion tracking based on optical surface monitoring by machine learning
Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Contrast-Enhanced MR and 3D Convolutional Neural Networks
Geometric and Dosimetric Evaluation of Deep Learning-Based Automatic Delineation on CBCT-Synthesized CT and Planning CT for Breast Cancer Adaptive Radiotherapy: A Multi-Institutional Study
Deep Learning-Based Automatic Delineation of Target Volumes and Organs at Risk of Breast Cancer for On-Line Dosimetric Evaluation
Prediction of Microvascular Invasion of Hepatocellular Carcinoma Based on Preoperative Diffusion-Weighted MR Using Deep Learning
Improving the malignancy characterization of hepatocellular carcinoma using deeply supervised cross modal transfer learning for non-enhanced MR
Similarity Steered Generative Adversarial Network and Adaptive Transfer Learning for Malignancy Characterization of Hepatocellualr Carcinoma