Deep learning detects actionable molecular and clinical features directly from head/neck squamous cell carcinoma histopathology slides
Published in International Journal of Radiation Oncology, Biology, Physics, 2020
Recommended citation: Deep learning detects actionable molecular and clinical features directly from head/neck squamous cell carcinoma histopathology slides. J. Dolezal, J.N. Kather, S. Kochanny, J. Schulte, A. Patel, B. Munyampirwa, S. Morin, A. Srisuwananukorn, N. Cipriani, D. Basu, A. Pearson. International Journal of Radiation Oncology, Biology, Physics, Volume 106, Issue 5, 1165 https://www.redjournal.org/article/S0360-3016(19)34202-6/abstract
The purpose of this abstract is to describe the application of deep learning to digital histopathology slide data for detection of clinically relevant features. Deep learning is a form of artificial intelligence which can process graphical data and “learn” to extract hidden features. Here we test the ability of deep learning to detect human papilloma virus, location of origin, and other features.
Citation: Deep learning detects actionable molecular and clinical features directly from head/neck squamous cell carcinoma histopathology slides. J. Dolezal, J.N. Kather, S. Kochanny, J. Schulte, A. Patel, B. Munyampirwa, S. Morin, A. Srisuwananukorn, N. Cipriani, D. Basu, A. Pearson. International Journal of Radiation Oncology, Biology, Physics, Volume 106, Issue 5, 1165