DeepAI AI Chat
Log In Sign Up

Accelerating COVID-19 Differential Diagnosis with Explainable Ultrasound Image Analysis

by   Jannis Born, et al.

Controlling the COVID-19 pandemic largely hinges upon the existence of fast, safe, and highly-available diagnostic tools. Ultrasound, in contrast to CT or X-Ray, has many practical advantages and can serve as a globally-applicable first-line examination technique. We provide the largest publicly available lung ultrasound (US) dataset for COVID-19 consisting of 106 videos from three classes (COVID-19, bacterial pneumonia, and healthy controls); curated and approved by medical experts. On this dataset, we perform an in-depth study of the value of deep learning methods for differential diagnosis of COVID-19. We propose a frame-based convolutional neural network that correctly classifies COVID-19 US videos with a sensitivity of 0.98+-0.04 and a specificity of 0.91+-08 (frame-based sensitivity 0.93+-0.05, specificity 0.87+-0.07). We further employ class activation maps for the spatio-temporal localization of pulmonary biomarkers, which we subsequently validate for human-in-the-loop scenarios in a blindfolded study with medical experts. Aiming for scalability and robustness, we perform ablation studies comparing mobile-friendly, frame- and video-based architectures and show reliability of the best model by aleatoric and epistemic uncertainty estimates. We hope to pave the road for a community effort toward an accessible, efficient and interpretable screening method and we have started to work on a clinical validation of the proposed method. Data and code are publicly available.


page 3

page 4

page 6

page 8

page 9

page 10

page 11

page 15


Accelerating Detection of Lung Pathologies with Explainable Ultrasound Image Analysis

Care during the COVID-19 pandemic hinges upon the existence of fast, saf...

POCOVID-Net: Automatic Detection of COVID-19 From a New Lung Ultrasound Imaging Dataset (POCUS)

With the rapid development of COVID-19 into a global pandemic, there is ...

Line Artefact Quantification in Lung Ultrasound Images of COVID-19 Patients via Non-Convex Regularisation

In this paper, we present a novel method for line artefacts quantificati...

Optimising Chest X-Rays for Image Analysis by Identifying and Removing Confounding Factors

During the COVID-19 pandemic, the sheer volume of imaging performed in a...

Adaptive Few-Shot Learning PoC Ultrasound COVID-19 Diagnostic System

This paper presents a novel ultrasound imaging point-of-care (PoC) COVID...

Code Repositories


This repository is our implementation of in Pytorch with some added features.

view repo