Share This Author
COVID-Net: a tailored deep convolutional neural network design for detection of COVID-19 cases from chest X-ray images
COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest X-ray (CXR) images that is open source and available to the general public, and COVIDx, an open access benchmark dataset comprising of 13,975 CXR images across 13,870 patient patient cases.
COVID-Net: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases from Chest Radiography Images
COVID-Net is introduced, a deep convolutional neural network design tailored for the detection of COVID-19 cases from chest radiography images that is open source and available to the general public and investigated how it makes predictions using an explainability method.
Fast YOLO: A Fast You Only Look Once System for Real-time Embedded Object Detection in Video
The evolutionary deep intelligence framework is leveraged to evolve the YOLOv2 network architecture and produce an optimized architecture that has 2.8X fewer parameters with just a ~2% IOU drop, and a motion-adaptive inference method is introduced into the proposed Fast Y OLO framework to reduce the frequency of deep inference with O-YOLO v2 based on temporal motion characteristics.
COVIDNet-CT: A Tailored Deep Convolutional Neural Network Design for Detection of COVID-19 Cases From Chest CT Images
COVIDNet-CT, a deep convolutional neural network architecture that is tailored for detection of COVID-19 cases from chest CT images via a machine-driven design exploration approach, is introduced and the model and dataset are introduced.
Lung Nodule Classification Using Deep Features in CT Images
- Devinder Kumar, A. Wong, David A Clausi
- Medicine, Computer Science12th Conference on Computer and Robot Vision
- 3 June 2015
This work proposes a CAD system which uses deep features extracted from an auto encoder to classify lung nodules as either malignant or benign, and uses 4303 instances containing 4323 nodules from the National Cancer Institute Lung Image Database Consortium (LIDC) dataset to obtain an overall accuracy.
Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection
- A. Wong, M. Shafiee, Francis Li, Brendan Chwyl
- Computer ScienceCanadian Conference on Computer and Robot Vision
- 19 February 2018
Tiny SSD is introduced, a single-shot detection deep convolutional neural network for real-time embedded object detection that is composed of a highly optimized, non-uniform Fire subnetwork stack and a non- uniform sub-network stack of highly optimized SSD-based auxiliary convolutionAL feature layers designed specifically to minimize model size while maintaining object detection performance.
Segmentation of Skin Lesions From Digital Images Using Joint Statistical Texture Distinctiveness
- J. Glaister, A. Wong, David A Clausi
- MedicineIEEE Transactions on Biomedical Engineering
- 6 January 2014
The proposed segmentation framework has higher segmentation accuracy compared to all other tested algorithms and is tested by comparing lesion segmentation results and melanoma classification results to results using other state-of-art algorithms.
ARRSI: Automatic Registration of Remote-Sensing Images
- A. Wong, David A Clausi
- Environmental Science, MathematicsIEEE Transactions on Geoscience and Remote…
- 23 April 2007
The results indicate that the registration accuracy of ARRSI is comparable to that produced by a human expert and improvement over the baseline and multimodal sum of squared differences registration techniques tested.
High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description
- R. Amelard, J. Glaister, A. Wong, David A Clausi
- Computer ScienceIEEE Transactions on Biomedical Engineering
- 1 March 2015
Experimental results show that concatenating the proposed high-level intuitive features with a full low-level feature set increased classification accuracy, and that HLIFs were able to separate the data better than low- level features with statistical significance.
A nonlocal-means approach to exemplar-based inpainting
This paper introduces a novel approach to the problem of image inpainting through the use of nonlocal image information from multiple samples within the image using an weighted similarity function and aggregated to form the missing information.