Machine Learning Methods for Image Analysis in Medical Applications, from Alzheimer's Disease, Brain Tumors, to Assisted Living
Doctoral thesis, 2020
convolutional neural networks
Alzheimer's disease detection
machine learning
deep learning
fall detection
glioma subtype classification
generative adversarial networks
recurrent convolutional networks
spiking neural networks
visual prosthesis
semi-supervised learning
Author
Chenjie Ge
Chalmers, Electrical Engineering, Signal Processing and Biomedical Engineering
Multi-Stream Multi-Scale Deep Convolutional Networks for Alzheimer's Disease Detection using MR Images
Neurocomputing,;Vol. 350(2019)p. 60-69
Journal article
Enlarged Training Dataset by Pairwise GANs for Molecular-Based Brain Tumor Classification
IEEE Access,;Vol. 8(2020)p. 22560-22570
Journal article
Deep Learning and Multi-Sensor Fusion for Glioma Classification Using Multistream 2D Convolutional Networks
Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS,;(2018)p. 5894-5897
Paper in proceeding
Deep semi-supervised learning for brain tumor classification
BMC Medical Imaging,;Vol. 20(2020)
Journal article
Human fall detection using segment-level CNN features and sparse dictionary learning
IEEE International Workshop on Machine Learning for Signal Processing, MLSP,;(2017)p. 6-
Paper in proceeding
Human Fall Detection using Co-Saliency-Enhanced Deep Recurrent Convolutional Neural Networks
Internationa Research Journal of Engineering and Technology (IRJET),;Vol. 6(2019)p. 993-1000
Journal article
Co-saliency detection via inter and intra saliency propagation
Signal Processing: Image Communication,;Vol. 44(2016)p. 69-83
Journal article
A spiking neural network model for obstacle avoidance in simulated prosthetic vision
Information Sciences,;Vol. 399(2017)p. 30-42
Journal article
Alzheimer's disease (AD) is a neural disease that happens mostly in the elderly, and memory loss is its main symptom. We address AD detection since early treatment of AD patients can slow down the progression of AD and reduce the symptoms. An effective method for diagnosing AD has been developed using Magnetic Resonance Images (MRIs). To learn the “features” from normal subjects and AD patients, we have proposed a new “deep learning” method, which automatically learns the changes caused by AD in grey matter (GM), white matter (WM) and cerebrospinal fluid (CSF) regions. These changes in MRIs are used as the features to distinguish AD patients from normal subjects. Our test results from a trained machine system has reached the state-of-the-art performance with more than 90% of AD patients detected. More tests should be conducted in the near future on larger datasets for possible clinical usage.
Another important application is to predict molecular-level types of brain tumor, glioma. Glioma is one of the most common types of original brain tumors. Prediction of glioma subtypes in the molecular level can lead to better treatment strategies. Usually a biopsy is taken from the brain of a patient for determining the tumor subtypes. It is a rather risky step as extracting tissues might lead to the loss of some brain functions such as hearing and vision. Hence, the process is usually performed while a patient is kept fully awake. By exploring deep learning methods to learn the molecular-level glioma subtype information from patients using their MRIs and biopsy information, our proposed method is shown promising to predict the glioma subtype by purely using MRIs from a new patient, without requiring a biopsy. The performance is rather encouraging, and it has reached the state-of-the-art (accuracy over 85% for one subtype, IDH mutation/wild-type prediction) though far from ideal. Further improvement of prediction performance is needed by improving the methods and by using a much larger number of MRIs to train the machine. We hope some improved deep learning methods can, in the near future, provide medical doctors with better semi-automatic ways for diagnosing brain tumors non-invasively.
Research on key Methods on Semi-Supervised Machine Learning of Big Data, with Applications to Assisted-Living in Elderly, Traffic Safety and Medical Diagnosis
The Swedish Foundation for International Cooperation in Research and Higher Education (STINT) (CH2015-6193), 2016-01-01 -- 2018-12-31.
Areas of Advance
Information and Communication Technology
Subject Categories
Neurosciences
Neurology
Computer Science
Radiology, Nuclear Medicine and Medical Imaging
Computer Vision and Robotics (Autonomous Systems)
Driving Forces
Innovation and entrepreneurship
ISBN
978-91-7905-322-2
Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4789
Publisher
Chalmers
"Femman" 5430, floor 5, EDIT building, Hörsalsvägen 11
Opponent: Prof. Danica Kragic Jensfelt, Royal Institute of Technology (KTH), Sweden