ArticlePloS one2022
Deep learning models for COVID-19 chest x-ray classification: Preventing shortcut learning using feature disentanglement.
Article in PloS one, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it, 20 citations in OpenAlex.
- Public Covid-19 X-ray datasets and their impact on model bias - A systematic review of a significant problem.Medical image analysis · 2021Pooled it
- Artificial Intelligence Research as a Continuous Clinical Service.Mayo Clinic proceedings. Digital health · 2026Review
- Article
- Detecting and Mitigating the Clever Hans Effect in Medical Imaging: A Scoping Review.Journal of imaging informatics in medicine · 2025Article
- Unmasking the Clever Hans effect in AI models: shortcut learning, spurious correlations, and the path toward robust intelligence.Frontiers in artificial intelligence · 2025Review
- Unmasking biases and navigating pitfalls in the ophthalmic artificial intelligence lifecycle: A narrative review.PLOS digital health · 2024Review
- AI pitfalls and what not to do: mitigating bias in AI.The British journal of radiology · 2023Review
- Development of Debiasing Technique for Lung Nodule Chest X-ray Datasets to Generalize Deep Learning Models.Sensors (Basel, Switzerland) · 2023Article
- Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice.Progress in retinal and eye research · 2022Review
- Automatic coronavirus disease 2019 diagnosis based on chest radiography and deep learning - Success story or dataset bias?Medical physics · 2022Article
- Augmenting existing deterioration indices with chest radiographs to predict clinical deterioration.PloS one · 2022Article
- Federated Learning for Microvasculature Segmentation and Diabetic Retinopathy Classification of OCT Data.Ophthalmology science · 2021Article
- American Society of Retina Specialists Artificial Intelligence Task Force Report.Journal of vitreoretinal diseasesArticle
Corrections and comments
- Update of
Authors and funding
12 authors at 3 institutions in 1 country.
Funding
Abstract
In response to the COVID-19 global pandemic, recent research has proposed creating deep learning based models that use chest radiographs (CXRs) in a variety of clinical tasks to help manage the crisis. However, the size of existing datasets of CXRs from COVID-19+ patients are relatively small, and researchers often pool CXR data from multiple sources, for example, using different x-ray machines in various patient populations under different clinical scenarios. Deep learning models trained on such datasets have been shown to overfit to erroneous features instead of learning pulmonary characteristics in a phenomenon known as shortcut learning. We propose adding feature disentanglement to the training process. This technique forces the models to identify pulmonary features from the images and penalizes them for learning features that can discriminate between the original datasets that the images come from. We find that models trained in this way indeed have better generalization performance on unseen data; in the best case we found that it improved AUC by 0.13 on held out data. We further find that this outperforms masking out non-lung parts of the CXRs and performing histogram equalization, both of which are recently proposed methods for removing biases in CXR datasets.
Indexed as
Identifiers
What OpenQuestion holds
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.