ArticleMedical image analysis2021
BS-Net: Learning COVID-19 pneumonia severity on a large chest X-ray dataset.
Article in Medical image analysis, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT05844215 (Role of MMP-9, TIMP-1 as Markers of Lung Imaging and Functional Abnormality of COVID-19), which is not on this map. Cited by 59 papers, 2 of them syntheses that pooled it.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Role of MMP-9, TIMP-1 as Markers of Lung Imaging and Functional Abnormality of COVID-19
Who cites it
59 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Role of Artificial Intelligence in COVID-19 Detection.Sensors (Basel, Switzerland) · 2021Pooled it
- Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.Medical image analysis · 2021Pooled it
- Large language models in emergency and critical care medicine: a comprehensive review of applications, challenges, and future directions.Burns & trauma · 2026Review
- CXR-LLaVA: a multimodal large language model for interpreting chest X-ray images.European radiology · 2025Article
- Machine Learning Techniques Applied to COVID-19 Prediction: A Systematic Literature Review.Bioengineering (Basel, Switzerland) · 2025Review
- TransMVAN: Multi-view Aggregation Network with Transformer for Pneumonia Diagnosis.Journal of imaging informatics in medicine · 2025Article
- Upskilling or deskilling? Measurable role of an AI-supported training for radiology residents: a lesson from the pandemic.Insights into imaging · 2025Article
- Article
- An open-source framework for end-to-end analysis of electronic health record data.Nature medicine · 2024Article
- Addressing fairness issues in deep learning-based medical image analysis: a systematic review.NPJ digital medicine · 2024Review
- Identifying severe community-acquired pneumonia using radiomics and clinical data: a machine learning approach.Scientific reports · 2024Article
- Reinvestigating the performance of artificial intelligence classification algorithms on COVID-19 X-Ray and CT images.iScience · 2024Article
- Exploratory analysis of serum Krebs von den Lungen-6, blood gas analysis & Brixia score in determining COVID-19 severity & mortality.The Indian journal of medical research · 2024Article
- Detection of Severe Lung Infection on Chest Radiographs of COVID-19 Patients: Robustness of AI Models across Multi-Institutional Data.Diagnostics (Basel, Switzerland) · 2024Article
- Improving deep neural network generalization and robustness to background bias via layer-wise relevance propagation optimization.Nature communications · 2024Article
- Improving explainable AI with patch perturbation-based evaluation pipeline: a COVID-19 X-ray image analysis case study.Scientific reports · 2023Article
- Augmentation strategies for an imbalanced learning problem on a novel COVID-19 severity dataset.Scientific reports · 2023Article
- Automatic Quantification of COVID-19 Pulmonary Edema by Self-supervised Contrastive Learning.Medical image learning with limited and noisy data : second international workshop, MILLanD 2023, held in conjunction with MICCAI 2023, Vancouver, BC, Canada, October 8, 2023, proceedings. MILLanD (Workshop) : (2nd : 2023 : Vancouver, B... · 2023Article
- COVID-19 Severity Prediction from Chest X-ray Images Using an Anatomy-Aware Deep Learning Model.Journal of digital imaging · 2023Article
- Multi-head deep learning framework for pulmonary disease detection and severity scoring with modified progressive learning.Biomedical signal processing and control · 2023Article
Corrections and comments
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Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
In this work we design an end-to-end deep learning architecture for predicting, on Chest X-rays images (CXR), a multi-regional score conveying the degree of lung compromise in COVID-19 patients. Such semi-quantitative scoring system, namely Brixia score, is applied in serial monitoring of such patients, showing significant prognostic value, in one of the hospitals that experienced one of the highest pandemic peaks in Italy. To solve such a challenging visual task, we adopt a weakly supervised learning strategy structured to handle different tasks (segmentation, spatial alignment, and score estimation) trained with a "from-the-part-to-the-whole" procedure involving different datasets. In particular, we exploit a clinical dataset of almost 5,000 CXR annotated images collected in the same hospital. Our BS-Net demonstrates self-attentive behavior and a high degree of accuracy in all processing stages. Through inter-rater agreement tests and a gold standard comparison, we show that our solution outperforms single human annotators in rating accuracy and consistency, thus supporting the possibility of using this tool in contexts of computer-assisted monitoring. Highly resolved (super-pixel level) explainability maps are also generated, with an original technique, to visually help the understanding of the network activity on the lung areas. We also consider other scores proposed in literature and provide a comparison with a recently proposed non-specific approach. We eventually test the performance robustness of our model on an assorted public COVID-19 dataset, for which we also provide Brixia score annotations, observing good direct generalization and fine-tuning capabilities that highlight the portability of BS-Net in other clinical settings. The CXR dataset along with the source code and the trained model are publicly released for research purposes.
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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.