Medical imaging data for digital diagnostics

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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.

Coronary computed tomography angiography (CCTA) of the heart and coronary vessels using contrast fluid and stress and determine whether they have been narrowed. Set of results.


Simon Doran is a Senior Staff Scientist in the Division of Radiotherapy and Imaging, Institute of Cancer Research in London, UK. His scientific interests encompass quantitative MRI, ultra-rapid MRI, optical computed tomography for 3-D microscopy, and radiotherapy dose mapping. His current responsibilities are centred around management of Cancer Research UK’s NCITA Image Repository Unit, and development of the XNAT imaging platform for use in multi-centre trials involving advanced imaging. Dr Doran has been an Editorial Board Member for Scientific Data since 2018.


Alba García Seco de Herrera is a Lecturer in the School of Computer Science and Electronic Engineering (CSEE) at the University of Essex, UK. Her research interest lies broadly in the area of Artificial Intelligence (AI) in computer vision with a special focus on biomedical imaging, neuroimaging, information retrieval and evaluation. She also casts the problem of Multimodal AI, in which various data types (image, text, speech, numerical data) are combined with multiple intelligence processing algorithms to achieve higher performances. Dr García Seco de Herrera has been an Editorial Board Member for Scientific Data since 2020.


Lena Maier-Hein is a Full Professor at Heidelberg University (Germany) and Managing Director of the National Center for Tumor Diseases (NCT) Heidelberg. At the German Cancer Research Center (DKFZ) she is Head of the Division Intelligent Medical Systems (IMSY) and Managing Director of the "Data Science and Digital Oncology" cross-topic program. Her research concentrates on machine learning-based biomedical image analysis with a specific focus on surgical data science, computational biophotonics and validation of machine learning algorithms. Professor Maier-Hein was an Editorial Board Member for Scientific Data until 2022.


Henning Müller is a Full Professor at the HES-SO Valais and since 2011 he has been responsible for the eHealth unit of the school. Since 2002, Henning has been working for the Medical Informatics Service at the University Hospital of Geneva. He is also a Professor at the Medical Faculty of the University of Geneva. Henning is coordinator of the ExaMode EU project and was previously coordinator of the Khresmoi EU project and scientific coordinator of the VISCERAL EU project. Professor Müller has been an Editorial Board Member for Scientific Data since 2019.