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IMAGE: The ground-truth and two enlarged lymph nodes are correctly detected, even though the lymph nodes are not annotated in the dataset. view more 

Credit: @SPIE

BELLINGHAM, Washington, USA and CARDIFF, UK – A paper published today in the Journal of Medical Imaging – “DeepLesion: Automated mining of large-scale lesion annotations and universal lesion detection with deep learning,” – announced the open availability of the largest CT lesion-image database accessible to the public. Such data are the foundations for the training sets of machine-learning algorithms; until now, large-scale annotated radiological image datasets, essential for the development of deep learning approaches, have not been publicly available.

DeepLesion, developed by a team from the National Institutes of Health Clinical Center, was developed by mining historical medical data from their own Picture Archiving and Communication System. This new dataset has tremendous potential to jump-start the field of computer-aided detection (CADe) and diagnosis (CADx).

The database includes multiple lesion types, including kidney lesions, bone lesions, lung nodules, and enlarged lymph nodes. The lack of a multi-category lesion dataset to date has been a major roadblock to development of more universal CADe frameworks capable of detecting multiple lesion types. A multi-category lesion dataset could

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