Deep learning powers cancer diagnosis in digital pathology.
Yunjie He, Hong Zhao, Stephen T C Wong
Author Information
Yunjie He: Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center, Houston, TX, 77030, USA.
Hong Zhao: Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center, Houston, TX, 77030, USA. Electronic address: hzhao@houstonmethodist.org.
Stephen T C Wong: Systems Medicine and Bioengineering Department, Houston Methodist Cancer Center, Houston, TX, 77030, USA. Electronic address: stwong@houstonmethodist.org.
Technological innovation has accelerated the pathological diagnostic process for cancer, especially in digitizing histopathology slides and incorporating deep learning-based approaches to mine the subvisual morphometric phenotypes for improving pathology diagnosis. In this perspective paper, we provide an overview on major deep learning approaches for digital pathology and discuss challenges and opportunities of such approaches to aid cancer diagnosis in digital pathology. In particular, the emerging graph neural network may further improve the performance and interpretability of deep learning in digital pathology.
