Obtaining Spatially Resolved Tumor Purity Maps Using Deep Multiple Instance Learning In A Pan-cancer Study
M. U. Oner, J. Chen, E. Revkov, A. James, S. Y. Heng, A. N. Kaya, J. J. S. Alvarez, A. Takano, X. M. Cheng, T. K. H. Lim, D. S. W. Tan, W. Zhai, A. Jacobsen Skanderup, W.-K. Sung, H. K. Lee
This paper develops a deep multiple instance learning model to predict tumor purity from histopathology slides, showing spatial variation and improving sample selection for genomic analysis.
bioRxiv · Jul 8, 2021Cancer Biology