Supervised machine learning (ML) algorithms require labeled data for algorithm training and validation.  What represents labeled data is a complex question that depends on the problem being addressed.  For example, an algorithm designed to analyze screening chest CT images and estimate the probability of a positive screening result (probability the person has cancer) may only need an outcome measure such as pathology confirmed cancer diagnosis, whereas a researcher focused on improving image segmentation would need accurate expert segmentations of the appropriate regions of interest.  TCIA continues to improve its support for Artificial Intelligence and Machine Learning based research by asking data submitters for more detailed information and improving our approaches to help researchers find the data they need.

Finding labeled datasets on TCIA

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