15 papers with code • 2 benchmarks • 1 datasets
We develop an algorithm that can detect pneumonia from chest X-rays at a level exceeding practicing radiologists.
Exploration of Interpretability Techniques for Deep COVID-19 Classification using Chest X-ray Images
The mean Micro-F1 score of the models for COVID-19 classifications ranges from 0. 66 to 0. 875, and is 0. 89 for the Ensemble of the network models.
Jointly Learning Convolutional Representations to Compress Radiological Images and Classify Thoracic Diseases in the Compressed Domain
Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain.
While existing approaches have achieved competitive performance in image classification, they are not well suited to problems where the computational budget is limited for two reasons: (1) the obtained architectures are either solely optimized for classification performance, or only for one deployment scenario; (2) the search process requires vast computational resources in most approaches.
To overcome this limitation, we present MUXConv, a layer that is designed to increase the flow of information by progressively multiplexing channel and spatial information in the network, while mitigating computational complexity.
One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image.
CovXNet: A multi-dilation convolutional neural network for automatic COVID-19 and other pneumonia detection from chest X-ray images with transferable multi-receptive feature optimization
Learning of this initial training phase is transferred with some additional fine-tuning layers that are further trained with a smaller number of chest X-rays corresponding to COVID-19 and other pneumonia patients.
MetaCOVID: A Siamese neural network framework with contrastive loss for n-shot diagnosis of COVID-19 patients
Various AI functionalities such as pattern recognition and prediction can effectively be used to diagnose (recognize) and predict coronavirus disease 2019 (COVID-19) infections and propose timely response (remedial action) to minimize the spread and impact of the virus.
The pitfalls of using open data to develop deep learning solutions for COVID-19 detection in chest X-rays
Model performance results have been exceptional when training and testing on open-source data, surpassing the reported capabilities of AI in pneumonia-detection prior to the COVID-19 outbreak.