Gleeson 2019 Automatic Gleason grading of prostate cancer in digital histopathology

These files are an Archive of the "Gleason 2019 Challenge". Please cite the original sources material and literature for the purpose of publication, and please use the original source download if/while it remains online.
Original Source: https://gleason2019.grand-challenge.org/Home/

Data used in this challenge consists of a set of tissue micro-array (TMA) images. Each TMA image is annotated in detail by several expert pathologists.

References

If you use this dataset, please cite the following:

Nir G, Hor S, Karimi D, Fazli L, Skinnider BF, Tavassoli P, Turbin D, Villamil CF, Wang G, Wilson RS, Iczkowski KA. Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts. Medical image analysis. 2018 Dec 1;50:167-80.

Karimi D, Nir G, Fazli L, Black PC, Goldenberg L, Salcudean SE. Deep Learning-Based Gleason Grading of Prostate Cancer From Histopathology Images—Role of Multiscale Decision Aggregation and Data Augmentation. IEEE journal of biomedical and health informatics. 2019 Sep 30;24(5):1413-26.