Mr. Hongwei Li (Bran)
My English name is Bran. I am a Ph.D. student in IBBM Group in Techinical University of Munich (TUM) supervised by Prof. Bjoern Menze started from Sep. 2017.
My research interests focus on machine learning and medical image analysis.
Before I was a visiting research student in School of Computing in University of Dundee for 6 months as a part of my master program in Sun Yat-sen University of China.
For more details, please have a look at my Curriculum Vitae and Google scholar
Current research projects for my Ph.D. program include:
1. Automated algorithms for classification of pancreas cysts using multi-modal computed tomography (CT);
2. Automatic segmentation of white matter hyperintensities (WMH) and multiple sclerosis (MS) lesions in MR images;
3. Brain age regression using MR fingerprinting and machine learning. This is a project in colaboration with INFN Pisa
4. Gene-radiomics study on 4D perfusion MR images.
(in colaboration with University College London and University of Dundee).
Available Projects for Master students and Undergraduates
Please feel free to contact me and Prof. Bjoern Menze if you are interested in relevant research topics on medical image analysis and computer vision, especially:
1. Weakly-supervised learning / one-shot learning in medical image analysis.
2. Deep learning's application on segmentation of lesion in MR images and lesion subtype classification using CT images.
WINNER (Team Name:sysu_media) of WMH Segmentation Challenge in MICCAI 2017! Detailed description of our method is submitted to NeuroImage Journal and now accepted!
1. Fully Convolutional Network Ensembles for White Matter Hyperintensities Segmentation in MR Images, NeuroImage
2. HEp-2 specimen classification via deep CNNs and pattern histogram, International Conference on Pattern Recognition (ICPR 2016)
3. HEp-2 cells staining patterns classification via wavelet scattering network and random forest, Asian Conference on Pattern Recognition (ACPR 2015)
Indoor volleyball, now playing for VfR Garching Men's volleyball team; Beach volleyball
updated on 09/07/2018