ArticleScientific reports2021
Validating deep learning inference during chest X-ray classification for COVID-19 screening.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
19 citing papers in PubMed.
- Enhancing Explainability in AI-Based Interstitial Lung Disease Detection via Multi-Class Learning Based on Lesion Areas.Journal of imaging informatics in medicine · 2026Article
- New Approaches in Radiotherapy.Cancers · 2025Review
- Artificial intelligence-based cardiac transthyretin amyloidosis detection and scoring in scintigraphy imaging: multi-tracer, multi-scanner, and multi-center development and evaluation study.European journal of nuclear medicine and molecular imaging · 2025Article
- Physics-Informed Autoencoder for Prostate Tissue Microstructure Profiling with Hybrid Multidimensional MRI.Radiology. Artificial intelligence · 2025Article
- RhizoNet segments plant roots to assess biomass and growth for enabling self-driving labs.Scientific reports · 2024Article
- Inconsistency between Human Observation and Deep Learning Models: Assessing Validity of Postmortem Computed Tomography Diagnosis of Drowning.Journal of imaging informatics in medicine · 2024Article
- Effective processing pipeline PACE 2.0 for enhancing chest x-ray contrast and diagnostic interpretability.Scientific reports · 2023Article
- A quantitative evaluation method utilizing the homology concept to assess the state of chromatin within the nucleus of lung cancer.Scientific reports · 2023Article
- How intra-source imbalanced datasets impact the performance of deep learning for COVID-19 diagnosis using chest X-ray images.Scientific reports · 2023Article
- Challenges of AI driven diagnosis of chest X-rays transmitted through smart phones: a case study in COVID-19.Scientific reports · 2023Article
- Lithium Metal Battery Quality Control via Transformer-CNN Segmentation.Journal of imaging · 2023Article
- MTSS-AAE: Multi-task semi-supervised adversarial autoencoding for COVID-19 detection based on chest X-ray images.Expert systems with applications · 2023Article
- Automated prediction of COVID-19 severity upon admission by chest X-ray images and clinical metadata aiming at accuracy and explainability.Scientific reports · 2023Article
- Conceptualising a channel-based overlapping CNN tower architecture for COVID-19 identification from CT-scan images.Scientific reports · 2022Article
- Audio texture analysis of COVID-19 cough, breath, and speech sounds.Biomedical signal processing and control · 2022Article
- Telemedicine and virtual respiratory care in the era of COVID-19.ERJ open research · 2022Review
- Machine learning applications for COVID-19 outbreak management.Neural computing & applications · 2022Review
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Authors and funding
4 authors.
Funding
Abstract
The new coronavirus unleashed a worldwide pandemic in early 2020, and a fatality rate several times that of the flu. As the number of infections soared, and capabilities for testing lagged behind, chest X-ray (CXR) imaging became more relevant in the early diagnosis and treatment planning for patients with suspected or confirmed COVID-19 infection. In a few weeks, proposed new methods for lung screening using deep learning rapidly appeared, while quality assurance discussions lagged behind. This paper proposes a set of protocols to validate deep learning algorithms, including our ROI Hide-and-Seek protocol, which emphasizes or hides key regions of interest from CXR data. Our protocol allows assessing the classification performance for anomaly detection and its correlation to radiological signatures, an important issue overlooked in several deep learning approaches proposed so far. By running a set of systematic tests over CXR representations using public image datasets, we demonstrate the weaknesses of current techniques and offer perspectives on the advantages and limitations of automated radiography analysis when using heterogeneous data sources.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.