Lung Cancer Diagnosis
6 papers with code • 0 benchmarks • 0 datasets
These leaderboards are used to track progress in Lung Cancer Diagnosis
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.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
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.
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train.
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks
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.
Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.
In cross-validation on screening chest CTs from the NLST, our methods (0. 785 and 0. 786 AUC respectively) significantly outperform a cross-sectional approach (0. 734 AUC) and match the discriminative performance of the leading longitudinal medical imaging algorithm (0. 779 AUC) on benign versus malignant classification.