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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.
A nonlocal-means approach to exemplar-based inpainting
  • A. Wong, Jeff Orchard
  • Mathematics, Computer Science
    15th IEEE International Conference on Image…
  • 12 December 2008
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.
Lung Nodule Classification Using Deep Features in CT Images
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.
Segmentation of Skin Lesions From Digital Images Using Joint Statistical Texture Distinctiveness
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
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.
Tiny SSD: A Tiny Single-Shot Detection Deep Convolutional Neural Network for Real-Time Embedded Object Detection
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.
High-Level Intuitive Features (HLIFs) for Intuitive Skin Lesion Description
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.
Efficient nonlocal-means denoising using the SVD
Experiments comparing this method against other NL-means speed-up strategies show that its refined discrimination between similar and dissimilar pixel neighbourhoods significantly improves the denoising effect.