In this study, we trained a deep convolutional neural network (inception v3) on whole-slide images obtained from The Cancer Genome Atlas to accurately and automatically classify them into LUAD, LUSC or normal lung tissue.
It achieved a kappa score of 0. 525 and an agreement of 66. 6% with three pathologists for classifying the predominant patterns, slightly higher than the inter-pathologist kappa score of 0. 485 and agreement of 62. 7% on this test set.
To the best of our knowledge, this is the first study to investigate capsule networks for making predictions based on radiologist-level interpretable attributes and its applications to medical image diagnosis.
Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train.