Browsing Systems Design Engineering by Subject "Deep Learning"
Now showing items 1-7 of 7
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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 ... -
Correlated Noise in Deep Convolutional Neural Networks
(University of Waterloo, 2019-08-28)This thesis explores one of the differences between the visual cortex and deep convolutional neural networks, namely, correlated fluctuations of neuron response strength. First, we describe the similarities and differences ... -
Deep Learning 3D Scans for Footwear Fit Estimation from a Single Depth Map
(University of Waterloo, 2018-01-02)In clothing and particularly in footwear, the variance in the size and shape of people and of clothing poses a problem of how to match items of clothing to a person. This is specifically important in footwear, as fit is ... -
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 ... -
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, ... -
Parallelizing Legendre Memory Unit Training
(University of Waterloo, 2021-07-14)Recently, a new recurrent neural network (RNN) named the Legendre Memory Unit (LMU) was proposed and shown to achieve state-of-the-art performance on several benchmark datasets. Here we leverage the linear time-invariant ... -
Regularizing Deep Models for Visual Recognition
(University of Waterloo, 2016-10-26)Image understanding is a shared goal in all computer vision problems. This objective includes decomposing the image into a set of primitive components through which one can perform region segmentation, region labeling, ...