AI-Assisted Diagnosis for Covid-19 CXR Screening: From Data Collection to Clinical Validation
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Abstract (arXiv)
In this paper, we present the major results from the Covid Radiographic imaging System based on AI (Co.R.S.A.) project, which took place in Italy. This project aims to develop a state-of-the-art AI-based system for diagnosing Covid-19 pneumonia from Chest X-ray (CXR) images. The contributions of this work are manyfold: the release of the public CORDA dataset, a deep learning pipeline for Covid-19 detection, and the clinical validation of the developed solution by expert radiologists. The proposed detection model is based on a two-step approach that, paired with state-of-the-art debiasing, provides reliable results. Most importantly, our investigation includes the actual usage of the diagnosis aid tool by radiologists, allowing us to assess the real benefits in terms of accuracy and time efficiency. Project homepage: https://corsa.di.unito.it/
Key facts. Cited by 1 paper in the map (data through 13 Oct 2026). Builds on 5 of its 9 references that are in the map. First posted on arXiv 19 May 2024.
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5 of 9 references.
References · 5
- 2016Densely Connected Convolutional Networkscited by 6,707
- 2019CheXpert: A Large Chest Radiograph Dataset with Uncertainty Labels and Expert Comparisoncited by 1,419
- 2019Machine Learning with Multi-Site Imaging Data: An Empirical Study on the Impact of Scanner Effectscited by 41
- 2022Unbiased Supervised Contrastive Learningcited by 40
- 2020Unveiling COVID-19 from Chest X-ray with deep learning: a hurdles race with small datacited by 20
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