Our model provides the basis for a clinically applicable deep-learning assistant to improve human efficiency and accuracy of CNS tumour diagnosis. The model will be made publicly available and could be implemented to assist human pathologists in future prospective studies.
[Articles] Classification accuracy of a hierarchical molecular inference-based deep-learning system for CNS tumour diagnosis: a multi-institutional, retrospective study
The Lancet Oncology | | H Lalchungnunga, Christopher H Dampier, Omkar Singh, Danh-Tai Hoang, Eldad D Shulman, Zied Abdullaev, Bochong Li, Zhirui Luo, Zhichao Wu, Thomas M Pearce, Daniel F Marker, Kathleen McCortney, Craig Horbinski, Calixto-Hope G Lucas, Patrick J Cimino, MacLean P Nasrallah, Martha Quezado, Hye-Jung Chung, Leeor Yefet, Gelareh Zadeh, Sebastian Brandner, Eytan Ruppin, Kenneth Aldape
Topics: skin-cancer, blood-cancer, sarcoma, research