Area of research
Radiology, Nuclear Medicine and Imaging · Hepatology
Research interest
Research interests include Medicine, Intravoxel incoherent motion, Radiomics, Magnetic resonance imaging, Hepatocellular carcinoma, and Neuroradiology.
Non-invasive tumor microenvironment evaluation and treatment response prediction in gastric cancer using deep learning radiomics
Intestinal fibrosis classification in patients with Crohn’s disease using CT enterography–based deep learning: comparisons with radiomics and radiologists
Facile Synthesis of Weakly Ferromagnetic Organogadolinium Macrochelates‐Based T<sub>1</sub>‐Weighted Magnetic Resonance Imaging Contrast Agents
Image-based deep learning identifies glioblastoma risk groups with genomic and transcriptomic heterogeneity: a multi-center study
IVIM using convolutional neural networks predicts microvascular invasion in HCC
Synthetic‐to‐real domain adaptation with deep learning for fitting the intravoxel incoherent motion model of diffusion‐weighted imaging
1284 Biology-guided deep learning predicts prognosis and cancer immunotherapy response
Transformer Based Multi-task Deep Learning with Intravoxel Incoherent Motion Model Fitting for Microvascular Invasion Prediction of Hepatocellular Carcinoma
An attention-based deep learning model for predicting microvascular invasion of hepatocellular carcinoma using an intra-voxel incoherent motion model of diffusion-weighted magnetic resonance imaging
Residual convolutional neural network for predicting response of transarterial chemoembolization in hepatocellular carcinoma from CT imaging
Radiomics signature of computed tomography imaging for prediction of survival and chemotherapeutic benefits in gastric cancer