ArticleJournal of the American Medical Informatics Association : JAMIA2023
ENRICHing medical imaging training sets enables more efficient machine learning.
Article in Journal of the American Medical Informatics Association : JAMIA, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 16 papers.
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16 citing papers in PubMed.
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- Self-supervised learning for label-free segmentation in cardiac ultrasound.Nature communications · 2025Article
- Artificial Intelligence to Enhance Precision Medicine in Cardio-Oncology: A Scientific Statement From the American Heart Association.Circulation. Genomic and precision medicine · 2025Review
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- Detailed Image Data Quality and Cleaning Practices for Artificial Intelligence Tools for Breast Cancer.JCO clinical cancer informatics · 2024Article
- Artificial Intelligence in Fluorescence Lifetime Imaging Ophthalmoscopy (FLIO) Data Analysis-Toward Retinal Metabolic Diagnostics.Diagnostics (Basel, Switzerland) · 2024Article
- Deep-learning model for prenatal congenital heart disease screening generalizes to community setting and outperforms clinical detection.Ultrasound in obstetrics & gynecology : the official journal of the International Society of Ultrasound in Obstetrics and Gynecology · 2024Article
- ChatGPT Helped Me Write This Talk Title, but Can It Read an Echocardiogram?Journal of the American Society of Echocardiography : official publication of the American Society of Echocardiography · 2023Article
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4 authors.
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Abstract
objectiveDeep learning (DL) has been applied in proofs of concept across biomedical imaging, including across modalities and medical specialties. Labeled data are critical to training and testing DL models, but human expert labelers are limited. In addition, DL traditionally requires copious training data, which is computationally expensive to process and iterate over. Consequently, it is useful to prioritize using those images that are most likely to improve a model's performance, a practice known as instance selection. The challenge is determining how best to prioritize. It is natural to prefer straightforward, robust, quantitative metrics as the basis for prioritization for instance selection. However, in current practice, such metrics are not tailored to, and almost never used for, image datasets. MATERIALS AND
methodsTo address this problem, we introduce ENRICH-Eliminate Noise and Redundancy for Imaging Challenges-a customizable method that prioritizes images based on how much diversity each image adds to the training set.
resultsFirst, we show that medical datasets are special in that in general each image adds less diversity than in nonmedical datasets. Next, we demonstrate that ENRICH achieves nearly maximal performance on classification and segmentation tasks on several medical image datasets using only a fraction of the available images and without up-front data labeling. ENRICH outperforms random image selection, the negative control. Finally, we show that ENRICH can also be used to identify errors and outliers in imaging datasets.
conclusionsENRICH is a simple, computationally efficient method for prioritizing images for expert labeling and use in DL.
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