Roger Tam

Associate Professor

Research Interests

Biomedical Informatics
Biomedical Technologies
Computer Science and Statistics
Data Analytics
Expert Systems
Machine Learning
Neurodegenerative diseases
Precision Medicine
Shape Recognition and Computer Graphics

Relevant Degree Programs


Research Methodology

Artificial Neural Networks
Deep Learning
Imaging Databases


Master's students
Doctoral students
I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.
I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.
I am open to hosting Visiting International Research Students (non-degree, up to 12 months).
I am interested in hiring Co-op students for research placements.

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Graduate Student Supervision

Doctoral Student Supervision (Jan 2008 - Nov 2020)
Deep learning for feature discovery in brain MRIs for patient-level classification with applications to multiple sclerosis (2018)

Network architectures and training strategies are crucial considerations in applying deep learning to neuroimaging data, but attaining optimal performance still remains challenging, because the images involved are high-dimensional and the pathological patterns to be modeled are often subtle. Additional challenges include limited annotations, heterogeneous modalities, and sparsity of certain image types. In this thesis, we have developed detailed methodologies to overcome these challenges for automatic feature extraction from multimodal neuroimaging data to perform image-level classification and segmentation, with applications to multiple sclerosis (MS).We developed our new methods in the context of four MS applications. The first was the development of an unsupervised deep network for MS lesion segmentation that was the first to use image features that were learned completely automatically, using unlabeled data. The deep-learned features were then refined with a supervised classifier, using a much smaller set of annotated images. We assessed the impact of unsupervised learning by observing the segmentation performance when the amount of unlabeled data was varied. Secondly, we developed an unsupervised learning method for modeling joint features from quantitative and anatomical MRIs to detect early MS pathology, which was novel in the use of deep learning to integrate high-dimensional myelin and structural images. Thirdly, we developed a supervised model that extracts brain lesion features that can predict conversion to MS in patients with early isolated symptoms. To efficiently train a convolutional neural network on sparse lesion masks and to reduce the risk of overfitting, we proposed utilizing the Euclidean distance transform for increasing information density, and a combination of downsampling, unsupervised pretraining and regularization during training. The fourth method models multimodal features between brain lesion and diffusion patterns to distinguish between MS and neuromyelitis optica, a neurological disorder similar to MS, to support differential diagnosis. We present a novel hierarchical multimodal fusion architecture that can improve joint learning of heterogeneous imaging modalities. Our results show that these models can discover subtle patterns of MS pathology and provide enhanced classification and prediction performance over the imaging biomarkers previously used in clinical studies, even with relatively small sample sizes. Supplementary materials available at:

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Efficient Deep Learning of 3D Structural Brain MRIs for Manifold Learning and Lesion Segmentation with Application to Multiple Sclerosis (2016)

Deep learning methods have shown great success in many research areas such asobject recognition, speech recognition, and natural language understanding, dueto their ability to automatically learn a hierarchical set of features that istuned to a given domain and robust to large variability. This motivates the useof deep learning for neurological applications, because the large variability inbrain morphology and varying contrasts produced by different MRI scanners makesthe automatic analysis of brain images challenging.However, 3D brain images pose unique challenges due to their complex contentand high dimensionality relative to the typical number of images available,making optimization of deep networks and evaluation of extracted featuresdifficult. In order to facilitate the training on large 3D volumes, we havedeveloped a novel training method for deep networks that is optimizedfor speed and memory. Our method performs training of convolutional deep beliefnetworks and convolutional neural networks in the frequency domain, whichreplaces the time-consuming calculation of convolutions with element-wisemultiplications, while adding only a small number of Fourier transforms.We demonstrate the potential of deep learning for neurological image analysisusing two applications. One is the development of a fully automatic multiplesclerosis (MS) lesion segmentation method based on a new type of convolutionalneural network that consists of two interconnected pathways for featureextraction and lesion prediction. This allows for the automatic learning offeatures at different scales that are optimized for accuracy for any givencombination of image types and segmentation task. Our network also uses a novelobjective function that works well for segmenting underrepresented classes, suchas MS lesions. The other application is the development of a statistical modelof brain images that can automatically discover patterns of variability in brainmorphology and lesion distribution. We propose building such a model using adeep belief network, a layered network whose parameters can be learned fromtraining images. Our results show that this model can automatically discover theclassic patterns of MS pathology, as well as more subtle ones, and that theparameters computed have strong relationships to MS clinical scores.

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Master's Student Supervision (2010 - 2018)
A Robust and Sensitive Voxel-Based Method for Measuring Cortical Thickness Change Using Fuzzy Correspondence (2015)

The human cerebral cortex is a deep folded structure of grey matter and is responsible for maintaining cognitive functions. The cortical thickness has emerged as an important surrogate biomarker in many neurodegenerative diseases. In this thesis, we propose a longitudinal method called LCT (Longitudinal Cortical Thickness) for measuring cortical thickness changes over time in magnetic resonance scans. We adopt a voxel-based approach rather than surface-based to gain computational efficiency and sensitivity to subtle changes but aim to achieve the scan-rescan reproducibility of surface-based methods. The existing surface-based methods are very time-consuming and the topological and smoothness constraints imposed during surface reconstruction may blunt longitudinal sensitivity. Also, longitudinal processing requires establishing anatomical correspondence across time, and most current methods use deformable registration for this purpose, which requires setting many parameters that can directly affect the measurements. In contrast, LCT establishes cortex-specific matches by using three intuitive features: normal direction, spatial coordinates and shape context that are defined on the cortical skeleton. We also introduce the concept of fuzzy correspondence, which allows a skeletal point in one scan to be partially matched to multiple points in another scan, thereby enhancing the stability of the matches. LCT was evaluated using three longitudinal datasets: 1) same-day pairs of scans of 15 subjects to test for scan-rescan reproducibility, 2) paired scans of 50 Alzheimer’s disease (AD) and 50 healthy subjects taken one-year apart, to test our method’s ability to distinguish between AD and normal, and 3) paired scans of 100 secondary progressive multiple sclerosis (MS) subjects taken two years apart, to test for sensitivity to change over time. Tests on the scan-rescan dataset show that LCT is comparable in scan-rescan reproducibility to two other state-of-the-art methods: FreeSurfer and minimum line integral (MLI). Tests on the AD dataset show that LCT detected larger group differences between AD and normal subjects than FreeSurfer and MLI. Further, the results on the MS dataset demonstrate that LCT is more sensitive to change over time and produced stronger correlations between cortical thickness changes and changes in clinical scores than FreeSurfer, which did not produce any statistically significant correlations.

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Investigating the Spinal Cord Atrophy Measurement on MRI from Two Aspects: Physiological Variations and Longitudinal Measurement Methods (2015)

Spinal cord atrophy is a valuable biomarker in multiple sclerosis (MS) for its significant correlation with physical disability. Measurement of spinal cord atrophy on MRI may be possibly confounded by fluctuations in water content, and the high measurement variance in previous longitudinal studies can be possibly reduced by registration-based methods. In this thesis, we investigated the effect of change in water content due to hydration status on cord cross-sectional area (CSA) measurement, and the applicability of three registration-based methods for longitudinal cord atrophy measurement. Our first hypothesis is that dehydration can decrease the cord CSA measurement on MRI. We found a mean decrease of 0.65% in CSA on scans collected from ten controls following a dehydration protocol using two independent cross-sectional CSA measurement methods. Our result demonstrates that change in water content of the cord is associated with measurable change in cord CSA. The second main hypothesis is that registration-based methods can decrease the variance in longitudinal cord atrophy measurement by using the signal from multiple scans to improve robustness to image noise and artifacts and by regularization of the registration to constrain the degrees of freedom. We implemented three algorithms: boundary shift integral based on rigid registration, Jacobian integration based on deformable registration and scale factor computation based on constrained registration (composed of rigid and scale transformation). We evaluated the three registration-based methods by comparing them to two cross-sectional methods, as applied to three longitudinal data sets: 1) images with simulated cord atrophy; 2) images acquired in the dehydration study described above; and 3) images of 15 MS patients over a two-year interval. Our main result was that while registration-based methods achieved more accurate results on simulation data sets and overall smaller measurement variance, they were not as sensitive, reporting no dehydration effect and smaller magnitude of patient cord atrophy. We argue that the limited spatial resolution of 1mm of MR scans in our experiment is possibly the main reason and future studies of cord atrophy measurement using registration-based methods should be conducted on MR scans with a high spatial resolution such as 0.5mm.

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Mathematical modeling of the spatial dispersion of brain MRI lesions in multiple sclerosis (2012)

Many previous studies in multiple sclerosis (MS) have focused on the relationship between white matter lesion volume and clinical parameters, but few have investigated the independent contribution of the spatial dispersion of lesions to patient disability.In this thesis, we investigate whether a mathematical measure of the 3D spatial dispersion of lesions can reveal clinical significance that is independent of volume. Our hypothesis is that for any two given patients with similar lesion loads, the one with greater lesion dispersion would tend to have a greater disability. We investigate four different approaches for quantifying lesion dispersion and examine the ability of these lesion dispersion measures to act as potential surrogate markers of disability. We propose one connectedness-based measure (compactness), two region-based measures (ratio of minimum bounding spheres and ratio of lesion convex hull to the brain volume), two distance-based measures (Euclidean distance from a fixed point and pair-wise Euclidean distances) and one measure based on network theory (small-worldness). Our data include three sets of MRIs (n = 24, 174, 182) selected from two MS clinical trials. We segment all white matter lesions in each scan with a semi-automatic method to produce binary images of lesion voxels, quantify their spatial dispersion using the defined measures, then perform a statistical analysis to compare the dispersion values to total lesion volume and patient disability. We use linear and rank correlations to investigate the relationships between dispersion, disability, and total lesion volume, and regression analysis to investigate whether there is a potentially meaningful relationship between dispersion and disability, independent of volume. Our main finding is that one distance based measure, Euclidean distance from a fixed point, consistently correlates with disability score across all three datasets, and has predictive value that is at least partly independent of lesion volume. The results provide support for our hypothesis and suggest that a potentially meaningful relationship exists between patient disability and measurements of lesion dispersion. Finding such relationships can improve the understanding of MS and potentially lead to the discovery of novel surrogate biomarkers for clinical use in designing treatment trials and providing prognostic advice to individual patients.

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