Lung Cancer Diagnosis

6 papers with code • 0 benchmarks • 0 datasets

This task has no description! Would you like to contribute one?

Most implemented papers

Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses

lalonderodney/X-Caps 12 Sep 2019

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

ncoudray/DeepPATH Nature Medicine 2018

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.

Synthetic Lung Nodule 3D Image Generation Using Autoencoders

SteveKommrusch/LuNG3D 19 Nov 2018

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

BMIRDS/deepslide 31 Jan 2019

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.

Knowledge-based Analysis for Mortality Prediction from CT Images

DIAL-RPI/KAMP-Net 20 Feb 2019

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

Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography

tom1193/time-distance-transformer 4 Sep 2022

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.