Machine Learning Methods for Image Analysis in Medical Applications, from Alzheimer's Disease, Brain Tumors, to Assisted Living
Doctoral thesis, 2020

Healthcare has progressed greatly nowadays owing to technological advances, where machine learning plays an important role in processing and analyzing a large amount of medical data. This thesis investigates four healthcare-related issues (Alzheimer's disease detection, glioma classification, human fall detection, and obstacle avoidance in prosthetic vision), where the underlying methodologies are associated with machine learning and computer vision. For Alzheimer’s disease (AD) diagnosis, apart from symptoms of patients, Magnetic Resonance Images (MRIs) also play an important role. Inspired by the success of deep learning, a new multi-stream multi-scale Convolutional Neural Network (CNN) architecture is proposed for AD detection from MRIs, where AD features are characterized in both the tissue level and the scale level for improved feature learning. Good classification performance is obtained for AD/NC (normal control) classification with test accuracy 94.74%. In glioma subtype classification, biopsies are usually needed for determining different molecular-based glioma subtypes. We investigate non-invasive glioma subtype prediction from MRIs by using deep learning. A 2D multi-stream CNN architecture is used to learn the features of gliomas from multi-modal MRIs, where the training dataset is enlarged with synthetic brain MRIs generated by pairwise Generative Adversarial Networks (GANs). Test accuracy 88.82% has been achieved for IDH mutation (a molecular-based subtype) prediction. A new deep semi-supervised learning method is also proposed to tackle the problem of missing molecular-related labels in training datasets for improving the performance of glioma classification. In other two applications, we also address video-based human fall detection by using co-saliency-enhanced Recurrent Convolutional Networks (RCNs), as well as obstacle avoidance in prosthetic vision by characterizing obstacle-related video features using a Spiking Neural Network (SNN). These investigations can benefit future research, where artificial intelligence/deep learning may open a new way for real medical applications.

glioma subtype classification

deep learning

spiking neural networks

recurrent convolutional networks

machine learning

semi-supervised learning

fall detection

Alzheimer's disease detection

visual prosthesis

generative adversarial networks

convolutional neural networks

"Femman" 5430, floor 5, EDIT building, Hörsalsvägen 11
Opponent: Prof. Danica Kragic Jensfelt, Royal Institute of Technology (KTH), Sweden

Author

Chenjie Ge

Chalmers, Electrical Engineering, Signalbehandling och medicinsk teknik, Imaging and Image Analysis

C. Ge, I.Y.H. Gu, A.S. Jakola, J. Yang, Deep Semi-Supervised Learning for Brain Tumor Classification.

Human fall detection using segment-level CNN features and sparse dictionary learning

IEEE International workshop on Machine learning for signal processing (MLSP 2017),; (2017)p. 6-

Paper in proceedings

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

This thesis investigates artificial intelligence and computer vision techniques with several healthcare-related applications. Among these applications, two most important ones are Alzheimer's disease detection and brain tumor classification.

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), 2016-01-01 -- 2018-12-31.

Areas of Advance

Information and Communication Technology

Driving Forces

Innovation and entrepreneurship

Subject Categories

Computer Science

Computer Vision and Robotics (Autonomous Systems)

ISBN

978-91-7905-322-2

Doktorsavhandlingar vid Chalmers tekniska högskola. Ny serie: 4789

Publisher

Chalmers University of Technology

"Femman" 5430, floor 5, EDIT building, Hörsalsvägen 11

Online

Opponent: Prof. Danica Kragic Jensfelt, Royal Institute of Technology (KTH), Sweden

More information

Latest update

6/23/2020