PhD Oral Exam - Ibrahim Ali Salim, Concordia Institute for Information Systems Engineering
Pediatric Bone Age Analysis and Brain Disease Prediction for Computer-Aided Diagnosis
This event is free
School of Graduate Studies
When studying for a doctoral degree (PhD), candidates submit a thesis that provides a critical review of the current state of knowledge of the thesis subject as well as the student’s own contributions to the subject. The distinguishing criterion of doctoral graduate research is a significant and original contribution to knowledge.
Once accepted, the candidate presents the thesis orally. This oral exam is open to the public.
Recent advances in 3D scanning technology have led to a widespread use of 3D shapes in a multitude of fields, including computer vision and medical imaging. These shapes are, however, often contaminated by noise, which needs to be removed or attenuated in order to ensure high-quality 3D shapes for subsequent use in downstream tasks. On the other hand, the availability of largescale pediatric hand radiographs and brain imaging benchmarks has sparked a surge of interest in designing efficient techniques for bone age assessment and brain disease prediction, which are fundamental problems in computer-aided diagnosis. Bone age is an effective metric for assessing the skeletal and biological maturity of children, while understanding how the brain develops is crucial for designing prediction models for the classification of brain disorders.
In this thesis, we present a feature-preserving framework for carpal bone surface denoising in the graph signal processing setting. The proposed denoising framework is formulated as a constrained optimization problem with an objective function comprised of a fidelity term specified by a noise model and a regularization term associated with data prior. We show through experimental results that our approach can remove noise effectively while preserving the nonlinear features of surfaces, such as curved surface regions and fine details. Moreover, recovering high quality surfaces from noisy carpal bone surfaces is of paramount importance to the diagnosis of wrist pathologies, such as arthritis and carpal tunnel syndrome. We also introduce a deep learning approach to pediatric bone age assessment using instance segmentation and ridge regression. This approach is comprised of two intertwined stages. In the first stage, we employ an image annotation and instance segmentation model to extract and separate different regions of interests in an image. In the second stage, we leverage the power of transfer learning by designing a deep neural network with a ridge regression output layer. For the classification of brain disorders, we propose an aggregator normalization graph convolutional network by exploiting aggregation in graph sampling, skip connections and identity mapping. We also integrate both imaging and non-imaging features into the graph nodes and edges, respectively, with the aim of augmenting predictive capabilities. We validate our proposed approaches through extensive experiments on various benchmark datasets, demonstrating competitive performance in comparison with strong baseline methods.