Computational Precision Medicine: Radiology-Pathology Challenge on Brain Tumor Classification 2019 (CPM-RadPath)
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.
Data Usage Agreement / Citations
You are free to use and/or refer to the CPM-RadPath datasets in your own research, provided that you always cite the following manuscript:
T. Kurc, S. Bakas, X. Ren, A. Bagari, A. Momeni, Y. Huang, L. Zhang, A. Kumar, M. Thibault, Q. Qi, Q. Wang, A. Kori, O. Gevaert, Y. Zhang, D. Shen, M. Khened, X. Ding, G. Krishnamurthi, J. Kalpathy-Cramer, J. Davis, T. Zhao, R. Gupta, J. Saltz, K. Farahani. "Segmentation and classification in digital pathology for glioma research: challenges and deep learning approaches". Frontiers in neuroscience, p.27, 2020.
Feel free to send any communication related to the CPM-RadPath challenge to firstname.lastname@example.org