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Data from TCIA collections have and continue to be used for image analysis challenges or competitions, e.g., image segmentation or tumor classification.  Specific challenges leveraging our data are described below. Please note that the challenges are not managed by TCIA staff, and in many cases you will be sent to web sites that are not affiliated with TCIA to learn more about them.

Data Science Bowl 2017

In the United States, lung cancer strikes 225,000 people every year, and accounts for $12 billion in health care costs. Early detection is critical to give patients the best chance at recovery and survival. Using a data set of thousands of high-resolution lung scans provided by the National Cancer Institute, participants developed algorithms that accurately determine when lesions in the lungs are cancerous. This will dramatically reduce the false positive rate that plagues the current detection technology, get patients earlier access to life-saving interventions, and give radiologists more time to spend with their patients. The challenge was hosted on Kaggle at:

PROSTATEx-2 Challenge 2017


SPIE, along with the support of the American Association of Physicists in Medicine (AAPM) and the National Cancer Institute (NCI), will conduct a “Grand Challenge” on quantitative image analysis methods for the diagnostic classification of clinically significant prostate lesions. As part of the 2017 SPIE Medical Imaging Symposium, the PROSTATEx Challenge will provide a unique opportunity for participants to compare their algorithms with those of others from academia, industry, and government in a structured, direct way using the same data sets. To learn more about the challenge please visit  To register for the challenge visit  

MICCAI 2016 – Computational Precision Medicine

The Computational Precision Medicine (CPM) will be a full-day satellite event held on October 21 in Athens, Greece at MICCAI 2016, composed of short workshops on advances in radio-path-omics and radiomics, and innovative challenges in CT radiomics, classification and nuclei segmentation in digital pathology, and mammographic CAD detection.

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