About me
I'm currently a 4th year Ph.D. student at <a href=https://uni-tuebingen.de/>Universität Tübingen</a> and Max Planck Institute for Biological Cybernetics. Prior to that, I recieved my master in major of Mechanical Engineering in Hong Kong Polytechnic University.
My current research falls in generative models using deep learning methods(e.g. GAN, score-match model, flow-based methods.) and its application on Medical Image data, such as MRI(~9.4Tesla). Apart from applications, I'm also working on the following topics:
- the convergence properties and training dynamics of GAN
- the application of optimal transport on deep learning tasks
- the alignment of machine intelligence with functional connectivity in human brain
Publications
Preprints
Improving the reliability of fMRI-based predictions of intelligence via semi-blind machine learning
Preprint, 2023
Gabriele Lohmann, Samuel Heczko, Lucas Mahler, Qi Wang, Julius Steiglechner, Vinod J. Kumar, Michelle Roost, Jürgen Jost, Klaus Scheffler
METAFormer: A Multi-Atlas Enhanced Transformer Architecture for Autism Spectrum Disorder Classification Using Resting-State fMRI
Preprint, 2023
Lucas Mahler, Qi Wang, Julius Steiglechner, Florian Birk, Samuel Heczko, Klaus Scheffler, Gabriele Lohmann
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging
Preprint, 2022
Qi Wang, Lucas Mahler, Julius Steiglechner, Florian Birk, Klaus Scheffler, Gabriele Lohmann
FLEXseg: Next Generation Brain MRI Segmentation at 9.4 T
Preprint, 2022
Julius Steiglechner, Qi Wang, Dana Ramadan, Lucas Mahler, Klaus Scheffler, Benjamin Bender, Tobias Lindig, Gabriele Lohmann
Conference papers
DISGAN: Wavelet-informede discriminator guides GAN to MRI images super-resolution with noise cleaning
ICCV2023 Workshop on Computer Vision for Automated Medical Diagnosis
Qi Wang, Lucas Mahler, Julius Steiglechner, Florian Birk, Klaus Scheffler, Gabriele Lohmann
A Three-player GAN for Super-Resolution in Magnetic Resonance Imaging
MICCAI2023 Workshop on Machine Learning for Clinical Neuroimaging
Qi Wang, Lucas Mahler, Julius Steiglechner, Florian Birk, Klaus Scheffler, Gabriele Lohmann
Pretraining is All You Need: A Multi-Atlas Enhanced Transformer Framework for Autism Spectrum Disorder Classification
MICCAI2023 Workshop on Machine Learning for Clinical Neuroimaging
Lucas Mahler, Qi Wang, Julius Steiglechner, Florian Birk, Samuel Heczko, Klaus Scheffler, Gabriele Lohmann
Super-Resolution for Ultra High-Field MR Images
Medical Imaging with Deep Learning (MIDL 2022, Zürich)
Qi Wang, Julius Steiglechner, Tobias Lindig, Benjamin Bender, Klaus Scheffler, Gabriele Lohmann
Synthetic 9T-like structural MRI using a Generative Neural Network
Neurowissenschaftliche Nachwuchskonferenz (NeNa 2021, Tübingen)
Qi Wang, Juliu Steiglechner, Gabriele Lohmann
Focal fMRI signal enhancement with implantable inductively coupled detectors
NeuroImage 2022
Yi Chen#, Qi Wang#, Sangcheon Choi, Hang Zeng, Kengo Takahashi, Chunqi Qian, Xin Yu#: Joint first author
Inductively coupled detectors for optogenetic-driven focal and multiregional fMRI signal enhancement
ISMRM 2021, summa cum laude award
Yi Chen#, Qi Wang#, Sangcheon Choi, Hang Zeng, Kengo Takahashi, Chunqi Qian, Xin Yu #: Joint first author
Real-Time fMRI Brain Mapping in Animals
Journal of Visualized Experiments, 2020
Sangcheon Choi, Kengo Takahashi, Yuanyuan Jiang, Sascha Köhler, Hang Zeng, Qi Wang, Yan Ma, Xin Yu
Talks
A Three-Player GAN for Super-Resolution in Magnetic Resonance Imaging
Oral presentation on MICCAI 2023 MLCN workshop, Paris, France
Deep learning for MRI super resolution and its applications
Max Planck Institute for Intelligent System, Tübingen, Germany
Super Resolution Improves Cortical Segmentation Accuracy in Ultra-high Resolution MRI
International Conference on Human Brain Mapping (OHBM) 2022, Glasgow, UK
Sythetic 9T-like structural MRI using Generative Neural Network
22nd Conference of Junior Neuroscientists (NeNa 2021), Tübingen, Germany