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Greatest papers with code

Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning

Nature Medicine 2018 ncoudray/DeepPATH

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.

CLASSIFICATION LUNG CANCER DIAGNOSIS WHOLE SLIDE IMAGES

Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks

31 Jan 2019BMIRDS/deepslide

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.

CLASSIFICATION IMAGE CLASSIFICATION LUNG CANCER DIAGNOSIS WHOLE SLIDE IMAGES

Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses

12 Sep 2019lalonderodney/X-Caps

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.

LUNG CANCER DIAGNOSIS MULTI-TASK LEARNING

Knowledge-based Analysis for Mortality Prediction from CT Images

20 Feb 2019DIAL-RPI/KAMP-Net

Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.

LUNG CANCER DIAGNOSIS MORTALITY PREDICTION

Synthetic Lung Nodule 3D Image Generation Using Autoencoders

19 Nov 2018SteveKommrusch/LuNG3D

One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train.

IMAGE GENERATION LUNG CANCER DIAGNOSIS