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Medical image and video datasets can support biomedical research through training machine learning algorithms, particularly via image recognition and classification. These can be applied to problems in digital health informatics, such as disease detection, diagnosis, and screening.
This Collection presents a series of articles describing annotated datasets of medical images and video. Data are presented without hypotheses or significant analyses, to support improvements such as benchmarking or improving machine learning algorithms. All medical specialities are considered and data can be derived from study participants, tissue samples, electronic health records (EHRs) or other sources. All described datasets are assessed to ensure their open availability (where possible) or secure access controls (where required) via Scientific Data's editorial and peer review processes.
Recent advances in computer-aided diagnosis, treatment response and prognosis in radiomics and deep learning challenge radiology with requirements for world-wide methodological standards for labeling, preprocessing and image acquisition protocols. The adoption of these standards in the clinical workflows is a necessary step towards generalization and interoperability of radiomics and artificial intelligence algorithms in medical imaging.