ArticlePattern recognition2021
Checklist for responsible deep learning modeling of medical images based on COVID-19 detection studies.
Article in Pattern recognition, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.
What it found
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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.
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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
9 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Explainable AI: A review of applications to neuroimaging data.Frontiers in neuroscience · 2022Pooled it
- A novel approach for assessing fairness in deployed machine learning algorithms.Scientific reports · 2024Article
- Leveraging Deep Learning Decision-Support System in Specialized Oncology Center: A Multi-Reader Retrospective Study on Detection of Pulmonary Lesions in Chest X-ray Images.Diagnostics (Basel, Switzerland) · 2023Article
- Prediction of the COVID disease using lung CT images by Deep Learning algorithm: DETS-optimized Resnet 101 classifier.Frontiers in medicine · 2023Article
- Explaining COVID-19 diagnosis with Taylor decompositions.Neural computing & applications · 2022Article
- FCF: Feature complement fusion network for detecting COVID-19 through CT scan images.Applied soft computing · 2022Article
- AI-Based human audio processing for COVID-19: A comprehensive overview.Pattern recognition · 2022Article
- Pareto optimization of deep networks for COVID-19 diagnosis from chest X-rays.Pattern recognition · 2022Article
- Using deep-learning in fetal ultrasound analysis for diagnosis of cystic hygroma in the first trimester.PloS one · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The sudden outbreak and uncontrolled spread of COVID-19 disease is one of the most important global problems today. In a short period of time, it has led to the development of many deep neural network models for COVID-19 detection with modules for explainability. In this work, we carry out a systematic analysis of various aspects of proposed models. Our analysis revealed numerous mistakes made at different stages of data acquisition, model development, and explanation construction. In this work, we overview the approaches proposed in the surveyed Machine Learning articles and indicate typical errors emerging from the lack of deep understanding of the radiography domain. We present the perspective of both: experts in the field - radiologists and deep learning engineers dealing with model explanations. The final result is a proposed checklist with the minimum conditions to be met by a reliable COVID-19 diagnostic model.
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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.