Browsing University of Waterloo by Author "Gaudet, Vincent C."
Now showing items 1-9 of 9
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Acceleration of Large Margin Metric Learning for Nearest Neighbor Classification Using Triplet Mining and Stratified Sampling
Poorhevari, Parisa Abdolrahim; Ghojogh, Benyamin; Gaudet, Vincent C.; Karray, Fakhri; Crowley, Mark (University of Waterloo, 2021-01-15)Metric learning is a technique in manifold learning to find a pro- jection subspace for increasing and decreasing the inter- and intra-class variances, respectively. Some metric learning methods are based on triplet learning ... -
Clinical Applications of Artificial Intelligence in positron emission tomography of Lung Cancer
Zukotynski, Katherine; Gaudet, Vincent C.; Uribe, Carlos F.; Chiam, Katarina; Benard, Francois; Gerbaudo, Victor (Elsevier, 2022-01)The ability of a computer to perform tasks normally requiring human intelligence or artificial intelligence (AI) is not new. However, until recently, practical applications in medical imaging were limited, especially in ... -
Exploratory Assessment of K-means Clustering to Classify 18F-Flutemetamol Brain PET as Positive or Negative
Zukotynski, Katherine; Black, Sandra E.; Kuo, Phillip H.; Bhan, Aparna; Adamo, Sabrina; Scott, Christopher J.M.; Lam, Benjamin; Masellis, Mario; Kumar, Sanjeev; Fischer, Corinne E.; Tartaglia, Maria Carmela; Lang, Anthony E.; Tang-Wai, David F.; Freedman, Morris; Vasdev, Neil; Gaudet, Vincent C. (Wolters Kluwer Health, 2021)Rationale: We evaluated K-means clustering to classify amyloid brain PETs as positive or negative. Patients and Methods: Sixty-six participants (31 men, 35 women; age range, 52–81 years) were recruited through a multicenter ... -
Hardware Implementation of a Fixed-Point Decoder for Low-Density Lattice Codes
Srivastava, Rachna; Gaudet, Vincent C.; Mitran, Patrick (Springer, 2022-01-31)This paper describes a field-programmable gate array (FPGA) implementation of a fixed-point low-density lattice code (LDLC) decoder where the Gaussian mixture messages that are exchanged during the iterative decoding process ... -
Machine Learning in the Nuclear Medicine: Part 1-Introduction
Uribe, Carlos F.; Mathotaarachchi, Sulantha; Gaudet, Vincent C.; Smith, Kenneth C.; Rosa-Neto, Pedro; Benard, Francois; Black, Sandra E.; Zukotynski, Katherine (Society of Nuclear Medicine and Molecular Imaging, 2019-04)This article, the first in a 2-part series, provides an introduction to machine learning (ML) in a nuclear medicine context. This part addresses the history of ML and describes common algorithms, with illustrations of when ... -
Machine Learning in the Nuclear Medicine: Part 2-Neural Networks and Clinical Aspects
Zukotynski, Katherine; Gaudet, Vincent C.; Uribe, Carlos F.; Mathotaarachchi, Sulantha; Smith, Kenneth C.; Rosa-Neto, Pedro; Benard, Francois; Black, Sandra E. (Society of Nuclear Medicine and Molecular Imaging, 2021-01)This article is the second part in our machine learning series. Part 1 provided a general overview of machine learning in nuclear medicine. Part 2 focuses on neural networks. We start with an example illustrating how neural ... -
Role of Artificial Intelligence in Theranostics: Toward Routine Personalized Radiopharmaceutical Therapies
Brosch-Lenz, Julia; Yousefirizi, Fereshteh; Zukotynski, Katherine; Beauregard, Jean-Mathieu; Gaudet, Vincent C.; Saboury, Babak; Rahmim, Arman; Uribe, Carlos F. (Elsevier, 2021-10)KEY POINTS AI has shown promising applications in quantitative imaging required for dosimetry. Segmentation of organs and tumors, the most time consuming task in the dosimetry workflow, can be automated using AI. ... -
The use of random forests to classify amyloid brain PET
Zukotynski, Katherine; Gaudet, Vincent C.; Kuo, Phillip H.; Adamo, Sabrina; Goubran, Maged; Scott, Christopher; Bocti, Christian; Borrie, Michael; Chertkow, Howard; Frayne, Richard; Hsiung, Robin; Laforce, Robert; Noseworthy, Michael D.; Prato, Frank S.; Sahlas, Demetrios J.; Smith, Eric E.; Sossi, Vesna; Thiel, Alexander; Soucy, Jean-Paul; Tardif, Jean-Claude; Black, Sandra E. (Wolters Kluwer Health, 2019-10)Purpose: To evaluate random forests (RFs) as a supervised machine learning algorithm to classify amyloid brain PET as positive or negative for amyloid deposition and identify key regions of interest for stratification. Methods: ... -
The Use of Random Forests to Identify Brain Regions on Amyloid and FDG PET Associated With MoCA Score
Zukotynski, Katherine; Gaudet, Vincent C.; Kuo, Phillip H.; Adamo, Sabrina; Goubran, Maged; Scott, Christopher J.M.; Bocti, Christian; Borrie, Michael; Chertkow, Howard; Frayne, Richard; Hsiung, Robin; Laforce, Robert Jr; Noseworthy, Michael D.; Prato, Frank S.; Sahlas, Demetrios J.; Smith, Eric E.; Sossi, Vesna; Thiel, Alexander; Soucy, Jean-Paul; Tardif, Jean-Claude; Black, Sandra E. (Wolters Kluwer Health, 2020-06)Purpose: The aim of this study was to evaluate random forests (RFs) to identify ROIs on 18F-florbetapir and 18F-FDG PET associated with Montreal Cognitive Assessment (MoCA) score. Materials and Methods: Fifty-seven subjects ...