ArticleJournal of digital imaging2017
Medical Image Data and Datasets in the Era of Machine Learning-Whitepaper from the 2016 C-MIMI Meeting Dataset Session.
Article in Journal of digital imaging, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 50 papers, 2 of them syntheses 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
50 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Diagnostic Performance of Machine Learning for Systemic Lupus Erythematosus: Systematic Review and Meta-Analysis.Journal of medical Internet research · 2026Pooled it
- The use and performance of artificial intelligence applications in dental and maxillofacial radiology: A systematic review.Dento maxillo facial radiology · 2020Pooled it
- On the public dissemination and open sourcing of ultrasound resources, datasets and deep learning models.NPJ digital medicine · 2025Article
- MIDAS: a technology-enabled hub-and-spoke system for the collection and dissemination of high-quality medical datasets in India.BMC medical informatics and decision making · 2025Article
- Advancing Medical Imaging Research Through Standardization: The Path to Rapid Development, Rigorous Validation, and Robust Reproducibility.Investigative radiology · 2025Review
- Assessing the documentation of publicly available medical image and signal datasets and their impact on bias using the BEAMRAD tool.Scientific reports · 2024Article
- Computational pathology: A survey review and the way forward.Journal of pathology informatics · 2024Review
- A Guideline for Open-Source Tools to Make Medical Imaging Data Ready for Artificial Intelligence Applications: A Society of Imaging Informatics in Medicine (SIIM) Survey.Journal of imaging informatics in medicine · 2024Article
- The Performance and Clinical Applicability of HER2 Digital Image Analysis in Breast Cancer: A Systematic Review.Cancers · 2024Review
- Artificial Intelligence and Multiple Sclerosis.Current neurology and neuroscience reports · 2024Review
- A head-to-head comparison of computed tomography- and magnetic resonance imaging-based radiomics in assessing pediatric peripheral neuroblastic tumor cell behavior.Abdominal radiology (New York) · 2024Article
- Annotation-efficient training of medical image segmentation network based on scribble guidance in difficult areas.International journal of computer assisted radiology and surgery · 2024Article
- Article
- Spatially localized sparse approximations of deep features for breast mass characterization.Mathematical biosciences and engineering : MBE · 2023Article
- Establishment and validation of an AI-aid method in the diagnosis of myocardial perfusion imaging.BMC medical imaging · 2023Article
- Artificial intelligence applications and ethical challenges in oral and maxillo-facial cosmetic surgery: a narrative review.Maxillofacial plastic and reconstructive surgery · 2023Review
- Semi-Supervised Segmentation Framework for Gastrointestinal Lesion Diagnosis in Endoscopic Images.Journal of personalized medicine · 2023Article
- CARRoT: R-package for predictive modelling by means of regression, adjusted for multiple regularisation methods.PloS one · 2023Article
- Application of Artificial Intelligence in Pathology: Trends and Challenges.Diagnostics (Basel, Switzerland) · 2022Review
- Effect of Training Data Volume on Performance of Convolutional Neural Network Pneumothorax Classifiers.Journal of digital imaging · 2022Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
At the first annual Conference on Machine Intelligence in Medical Imaging (C-MIMI), held in September 2016, a conference session on medical image data and datasets for machine learning identified multiple issues. The common theme from attendees was that everyone participating in medical image evaluation with machine learning is data starved. There is an urgent need to find better ways to collect, annotate, and reuse medical imaging data. Unique domain issues with medical image datasets require further study, development, and dissemination of best practices and standards, and a coordinated effort among medical imaging domain experts, medical imaging informaticists, government and industry data scientists, and interested commercial, academic, and government entities. High-level attributes of reusable medical image datasets suitable to train, test, validate, verify, and regulate ML products should be better described. NIH and other government agencies should promote and, where applicable, enforce, access to medical image datasets. We should improve communication among medical imaging domain experts, medical imaging informaticists, academic clinical and basic science researchers, government and industry data scientists, and interested commercial entities.
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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.