Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification 2019 (CPM-RadPath)

• Motivation • DataParticipation SummaryRegistrationPeople


15 Jul
19 Aug
15 Sep (Extended)
3 Sep - 18 Sep
20 Sep
17 Oct
15 Nov

 (All deadlines are for 23:59 Eastern Time)
Release of training datasets.
Release of validation datasets.
Submission of short papers, reporting proposed method & preliminary results.
Release of testing datasets for 48hr window (& performance evaluation).
Contacting top performing methods for preparing slides for oral presentation.
Challenge at MICCAI (China).
Extended LNCS paper submission deadline.


Brain cancer is a fatal and complex disease. Diagnosis and grading of brain tumors is traditionally done by pathologists, who examine tissue sections fixed on glass slides under a light microscope. While this process continues to be widely applied in clinical setting, it is not scalable to translational and clinical research studies involving hundreds or thousands of tissue specimens. Computer-aided classification has the potential to improve tumor diagnosis and grading process, as well as to enable quantitative studies of the mechanisms underlying disease onset and progression.

The challenge will evaluate the performance of automated classification algorithms when information from two types of imaging data – Radiology images and Pathology images – is used. Participants are asked to classify a cohort of brain tumor cases into three sub-types: Glioblastoma, Oligodendroglioma, and Astrocytoma. More details about the data can be found in the "Data" page.

The objective of this challenge is two-fold: 1) to evaluate and compare classification algorithms and 2) to encourage the design and implementation of accurate and efficient algorithms.



CPM-RadPath 2019 runs in conjunction with the MICCAI 2019 conference, on Oct.17, as part of the full-day BrainLes Workshop.

Feel free to send any communication related to the CPM-RadPath challenge to