Li, Hongming

Cancer imaging phenomics toolkit: quantitative imaging analytics for precision diagnostics and predictive modeling of clinical outcome

Davatzikos, C., Rathore, S., Bakas, S., Pati, S., Bergman, M., Kalarot, R., Sridharan, P., Gastounioti, A., Jahani, N., Cohen, E., et al. (2018) · Journal of Medical Imaging

Radiomics and Machine Learning in Medical ImagingMRI in cancer diagnosis

Cited by 204

The Cancer Imaging Phenomics Toolkit (CaPTk): Technical Overview

Pati, S., Singh, A., Rathore, S., Gastounioti, A., Bergman, M., Ngo, P., Ha, S. M., Bounias, D., Minock, J., Murphy, G., et al. (2020) · Lecture Notes in Computer Science

Radiomics and Machine Learning in Medical ImagingMedical Image Segmentation Techniques

Cited by 94

Early Prediction Of Alzheimer’s Disease Dementia Based On Baseline Hippocampal MRI and 1-Year Follow-Up Cognitive Measures Using Deep Recurrent Neural Networks

Li, H., & Fan, Y. (2019) · 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019)

Dementia and Cognitive Impairment ResearchAlzheimer's disease research and treatments

Cited by 83

Brain Decoding from Functional MRI Using Long Short-Term Memory Recurrent Neural Networks

Li, H., & Fan, Y. (2018) · Lecture Notes in Computer Science

Functional Brain Connectivity StudiesEEG and Brain-Computer Interfaces

Cited by 45

Identification of Temporal Transition of Functional States Using Recurrent Neural Networks from Functional MRI

Li, H., & Fan, Y. (2018) · Lecture Notes in Computer Science

Functional Brain Connectivity StudiesAdvanced MRI Techniques and Applications

Cited by 13