In Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
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.
He Yunjie, Zhao Hong, Wong Stephen T C
AI, Digital pathology, cancer diagnosis, deep learning, graph neural networks, microscopy image