Data Analysis Strategies in Medical Imaging
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Summary
Recommendations are provided for analysis strategies of medical imaging data, including data normalization, development of robust models, and rigorous statistical analyses that will not only improve analysis quality but also enhance precision medicine by allowing better integration of imaging data with other biomedical data sources.
- Type
- review
- Published
- 2018-03-26
- Cited by
- 140
- References
- 65
- Access
- Open access
- OpenAlex
- https://openalex.org/W2789242863
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:4389954
Keywords
Medical physics, Medical imaging, Sophistication, Medicine, Data science
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- Big Data and Clinicians: A Review on the State of the Science
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- From Handcrafted to Deep-Learning-Based Cancer Radiomics: Challenges and opportunities
- An overview of deep learning in medical imaging focusing on MRI
- Application of machine learning in rheumatic disease research
- Texture Analysis of Imaging: What Radiologists Need to Know.
- Artificial intelligence in Cancer Imaging: Clinical Challenges and Applications
- Machine (Deep) Learning Methods for Image Processing and Radiomics
- Functional logistic discrimination with sparse PCA and its application to the structural MRI
- Deep Learning Predicts Lung Cancer Treatment Response from Serial Medical Imaging
- What can artificial intelligence teach us about the molecular mechanisms underlying disease?
- Deep Learning to Assess Long-term Mortality From Chest Radiographs
- Artificial intelligence in the interpretation of breast cancer on MRI
- Convolution kernel and iterative reconstruction affect the diagnostic performance of radiomics and deep learning in lung adenocarcinoma pathological subtypes
- Statistical considerations for testing an AI algorithm used for prescreening lung CT images
- Prediction of outcome in Parkinson’s disease patients from DAT SPECT images using a convolutional neural network
- L’intelligence artificielle au service de l’imagerie et de la santé des femmes
- An Analysis for Key Indicators of Reproducibility in Radiology
- System-based approaches as prognostic tools for glioblastoma
- PET/CT radiomics in breast cancer: mind the step.
- Searching for biomarkers of disease-free survival in head and neck cancers using PET/CT radiomics
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