Browsing Theses by Supervisor "Tizhoosh, Hamid"
Now showing items 1-14 of 14
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Content-based Image Retrieval of Gigapixel Histopathology Scans: A Comparative Study of Convolution Neural Network, Local Binary Pattern, and Bag of visual Words
(University of Waterloo, 2018-05-04)The state-of-the-art image analysis algorithms offer a unique opportunity to extract semantically meaningful features from medical images. The advantage of this approach is automation in terms of content-based image retrieval ... -
A Deep-Learning Framework for Detecting and Predicting Clinical Events Using Continuous, Multimodal Physiological Signals
(University of Waterloo, 2024-02-20)There are an estimated 313 million surgeries performed worldwide each year. Even with significant clinical and technical advances in perioperative research, many patients experience a major complication during the first ... -
Harmonizing the Scale: An End-to-End Self-Supervised Approach for Cross-Modal Data Retrieval in Histopathology Archives
(University of Waterloo, 2023-09-18)In recent years, the exponential growth of data across various domains has necessitated the development of advanced techniques to process and analyze multi-modal big data. This is particularly relevant in the medical domain ... -
KimiaNet: Training a Deep Network for Histopathology using High-Cellularity
(University of Waterloo, 2020-09-11)With the recent progress in deep learning, one of the common approaches to represent images is extracting deep features. A primitive way to do this is by using off-the-shelf models. However, these features could be improved ... -
Learning Compact Representations for Efficient Whole Slide Image Search in Computational Pathology
(University of Waterloo, 2022-08-24)Digital pathology has enabled us to capture, store, query and analyze scanned biopsy samples as digital images. The widespread adoption of digital pathology has spurred the digitization of tissue biopsy samples, known as ... -
Learning Discriminative Representations for Gigapixel Images
(University of Waterloo, 2022-05-11)Digital images of tumor tissue are important diagnostic and prognostic tools for pathologists. Recent advancement in digital pathology has led to an abundance of digitized histopathology slides, called whole-slide images. ... -
Multi-Magnification Search in Digital Pathology
(University of Waterloo, 2021-09-21)This research study investigates the effect of magnification on content-based image search in digital pathology archives and proposes to use multi-magnification image representation. Image search in large archives of digital ... -
Multimodal Artificial Intelligence for Histopathology & Genomics Fusion
(University of Waterloo, 2024-01-29)The field of medical diagnostics has witnessed a transformative convergence of artificial intelligence (AI) and healthcare data, offering promising avenues for enhancing patient care and disease comprehension. However, ... -
Out-of-Distribution Generalization of Gigapixel Image Representation
(University of Waterloo, 2023-08-18)This thesis addresses the significant challenge of improving the generalization capabilities of artificial deep neural networks in the classification of whole slide images (WSIs) in histopathology across different and ... -
Radon Projections as Image Descriptors for Content-Based Retrieval of Medical Images
(University of Waterloo, 2018-04-23)Clinical analysis and medical diagnosis of diverse diseases adopt medical imaging techniques to empower specialists to perform their tasks by visualizing internal body organs and tissues for classifying and treating diseases ... -
Recognizing Magnification Levels in Microscopic Snapshots using Machine Learning
(University of Waterloo, 2020-09-25)State-of-of-the-art computer vision research has facilitated technology evolution in the field of medical imaging. The primary achievement of the imaging algorithms developed is the extraction of expressive features from ... -
Representation Learning for Image Search in Histopathology
(University of Waterloo, 2024-01-26)Advancements in the field of Machine Learning (ML) have shown significant promise in complementing the endeavors of healthcare professionals. However, the widespread acceptance and trust in clinical applications necessitate ... -
A Self-Supervised Contrastive Learning Approach for Whole Slide Image Representation in Digital Pathology
(University of Waterloo, 2022-05-16)Digital pathology has recently expanded the field of medical image processing for di- agnostic reasons. Whole slide images (WSIs) of histopathology are often accompanied by information on the location and type of diseases ... -
Set Representation Learning: A Framework for Learning Gigapixel Images
(University of Waterloo, 2021-09-13)In Machine Learning, we often encounter data as a set of instances such Point Clouds (set of x,y, and z coordinates), patches from gigapixel images (Digital Pathology, Satellite Imagery, Astronomical Images, etc.), Weakly ...