Lung Cancer Diagnosis
8 papers with code • 0 benchmarks • 0 datasets
Benchmarks
These leaderboards are used to track progress in Lung Cancer Diagnosis
Latest papers
Variational Autoencoders for Feature Exploration and Malignancy Prediction of Lung Lesions
benkeel/vae_lung_lesion_bmvc
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Proposed models were trained on lesions extracted from 3D CT scans in the LIDC-IDRI public dataset.
Specialty-Oriented Generalist Medical AI for Chest CT Screening
niuchuangnn/m3fm
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Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology.
Time-distance vision transformers in lung cancer diagnosis from longitudinal computed tomography
tom1193/time-distance-transformer
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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.
Encoding Visual Attributes in Capsules for Explainable Medical Diagnoses
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.
Knowledge-based Analysis for Mortality Prediction from CT Images
Recent studies have highlighted the high correlation between cardiovascular diseases (CVD) and lung cancer, and both are associated with significant morbidity and mortality.
Pathologist-level classification of histologic patterns on resected lung adenocarcinoma slides with deep neural networks
BMIRDS/deepslide
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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.
Synthetic Lung Nodule 3D Image Generation Using Autoencoders
One of the challenges of using machine learning techniques with medical data is the frequent dearth of source image data on which to train.
Classification and mutation prediction from non–small cell lung cancer histopathology images using deep learning
ncoudray/DeepPATH
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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.
