Zhicheng Jiao, Ph.D.

Postdoctoral Researcher

Zhicheng JiaoArtificial Intelligence in Biomedical Imaging Lab (AIBIL)
Center for Biomedical Image Computing & Analytics (CBICA)
Department of Radiology
Perelman School of Medicine
University of Pennsylvania



Richards Labs, Suite 700D
3700 Hamilton Walk
Philadelphia, PA 19104


Educational Background

Ph.D. in Intelligent Information Processing, Xidian University, China
B.S. in Electronic Information Engineering, Xidian University, China

Research Interests

Deep learning, Medical image analysis, Computer-aided diagnosis, Brain decoding


For a full list of publications you can go to: Google Scholar


Improving Diagnosis of Autism Spectrum Disorder and Disentangling its Heterogeneous Functional Connectivity Patterns using Capsule Networks. ISBI 2020 (Oral)

Hybrid Graph Neural Networks for Crowd Counting. AAAI 2020.

Dynamic Routing Capsule Networks for Mild Cognitive Impairment Diagnosis. MICCAI 2019

Pre-operative Overall Survival Time Prediction for Glioblastoma Patients Using Deep Learning on Both Imaging Phenotype and Genotype. MICCAI 2019

An Attention-Guided Deep Regression Model for Landmark Detection in Cephalograms. MICCAI 2019

A Deep Learning Framework for Noise Component Detection from Resting-state Functional MRI. MICCAI 2019

CoCa-GAN: Common-feature-learning-based Context-aware Generative Adversarial Network for Glioma Grading. MICCAI 2019

Refined-Segmentation R-CNN: A Two-stage Convolutional Neural Network for Punctate White Matter Lesion Segmentation in Preterm Infants. MICCAI 2019

Decoding EEG by Visual-guided Deep Neural Networks. IJCAI 2019 (Oral)


Integrating radiomics and circulating tumor cell analysis improves prediction of treatment outcomes of early stage NSCLC patients treated with SBRT. ASTRO 2020 (Oral)

Integration of risk of survival measures estimated from pre- and post-treatment CT scans improves stratification of early stage non-small cell lung cancer patients treated with stereotactic body radiation therapy. ASTRO 2020.

Learning Effective Radiomic Features for Characterization of Breast Lesions with Multi-b Diffusion-Weighted MR Imaging. RSNA 2019 (Oral)

Comparison of Four Radiomics-based Classification Methods in Diagnosis of Breast Lesions with Multi-b Diffusion-Weighted MR Imaging. RSNA 2019 (Oral)

Common-space-learning from Multi-modality for Missing MRI Synthesis and Glioma Grading. RSNA 2019.