8 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.
Ranked #3 on Pneumonia Detection on ChestX-ray14
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.
Ranked #1 on Pneumonia Detection on ChestX-ray14
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.
Ranked #2 on Neural Architecture Search on CIFAR-10
One of the primary clinical observations for screening the infectious by the novel coronavirus is capturing a chest x-ray image.
Ranked #1 on Multi-class Classification on COVID-19 CXR Dataset
Deep learning models trained in natural images are commonly used for different classification tasks in the medical domain.
Ranked #5 on Pneumonia Detection on ChestX-ray14
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.
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.