ArticleScientific reports2021
Data valuation for medical imaging using Shapley value and application to a large-scale chest X-ray dataset.
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 13 papers.
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Who cites it
13 citing papers in PubMed.
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- Naïve Bayes is an interpretable and predictive machine learning algorithm in predicting osteoporotic hip fracture in-hospital mortality compared to other machine learning algorithms.PLOS digital health · 2025Article
- An initial game-theoretic assessment of enhanced tissue preparation and imaging protocols for improved deep learning inference of spatial transcriptomics from tissue morphology.Briefings in bioinformatics · 2024Article
- Evaluation of perceived urgency from single-trial EEG data elicited by upper-body vibration feedback using deep learning.Scientific reports · 2024Article
- Generalization-a key challenge for responsible AI in patient-facing clinical applications.NPJ digital medicine · 2024Article
- Data Valuation with Gradient Similarity.ArXiv · 2024Article
- MedShift: Automated Identification of Shift Data for Medical Image Dataset Curation.IEEE journal of biomedical and health informatics · 2023Article
- Development and validation of interpretable machine learning models for inpatient fall events and electronic medical record integration.Clinical and experimental emergency medicine · 2022Article
- Towards More Efficient Data Valuation in Healthcare Federated Learning using Ensembling.Distributed, collaborative, and federated learning, and affordable AI and healthcare for resource diverse global health : Third MICCAI Workshop, DeCaF 2022 and Second MICCAI Workshop, FAIR 2022, held in conjunction with MICCAI 2022, Sin... · 2022Article
- Review on COVID-19 diagnosis models based on machine learning and deep learning approaches.Expert systems · 2022Article
- Using Deep Neural Networks for Predicting Age and Sex in Healthy Adult Chest Radiographs.Journal of clinical medicine · 2021Article
- Data analysis with Shapley values for automatic subject selection in Alzheimer's disease data sets using interpretable machine learning.Alzheimer's research & therapy · 2021Article
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7 authors.
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Abstract
The reliability of machine learning models can be compromised when trained on low quality data. Many large-scale medical imaging datasets contain low quality labels extracted from sources such as medical reports. Moreover, images within a dataset may have heterogeneous quality due to artifacts and biases arising from equipment or measurement errors. Therefore, algorithms that can automatically identify low quality data are highly desired. In this study, we used data Shapley, a data valuation metric, to quantify the value of training data to the performance of a pneumonia detection algorithm in a large chest X-ray dataset. We characterized the effectiveness of data Shapley in identifying low quality versus valuable data for pneumonia detection. We found that removing training data with high Shapley values decreased the pneumonia detection performance, whereas removing data with low Shapley values improved the model performance. Furthermore, there were more mislabeled examples in low Shapley value data and more true pneumonia cases in high Shapley value data. Our results suggest that low Shapley value indicates mislabeled or poor quality images, whereas high Shapley value indicates data that are valuable for pneumonia detection. Our method can serve as a framework for using data Shapley to denoise large-scale medical imaging datasets.
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