Lung Nodule Detection
9 papers with code • 2 benchmarks • 3 datasets
Benchmarks
These leaderboards are used to track progress in Lung Nodule Detection
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LIDC-IDRI
LUNA
LUNA16
Latest papers with no code
X-ray Dissectography Improves Lung Nodule Detection
Although radiographs are the most frequently used worldwide due to their cost-effectiveness and widespread accessibility, the structural superposition along the x-ray paths often renders suspicious or concerning lung nodules difficult to detect.
Debiasing pipeline improves deep learning model generalization for X-ray based lung nodule detection
In stripping chest X-ray images of known confounding variables by lung field segmentation, along with suppression of signal noise from the bone structure we can train a highly accurate deep learning lung nodule detection algorithm with outstanding generalization accuracy of 89% to nodule samples in unseen data.
Continual Active Learning Using Pseudo-Domains for Limited Labelling Resources and Changing Acquisition Characteristics
Here, we propose a method for continual active learning operating on a stream of medical images in a multi-scanner setting.
Dual Skip Connections Minimize the False Positive Rate of Lung Nodule Detection in CT images
Pulmonary cancer is one of the most commonly diagnosed and fatal cancers and is often diagnosed by incidental findings on computed tomography.
CT-SGAN: Computed Tomography Synthesis GAN
Diversity in data is critical for the successful training of deep learning models.
AWEU-Net: An Attention-Aware Weight Excitation U-Net for Lung Nodule Segmentation
Accurate lung nodule detection and segmentation in computed tomography (CT) images is the most important part of diagnosing lung cancer in the early stage.
Unsupervised Detection of Lung Nodules in Chest Radiography Using Generative Adversarial Networks
External validation and testing are performed using healthy and unhealthy patches extracted from the ChestX-ray14 and Japanese Society for Radiological Technology datasets, respectively.
End-to-end lung nodule detection framework with model-based feature projection block
This paper proposes novel end-to-end framework for detecting suspicious pulmonary nodules in chest CT scans.
Exploring Instance-Level Uncertainty for Medical Detection
The ability of deep learning to predict with uncertainty is recognized as key for its adoption in clinical routines.
Deep Mining Generation of Lung Cancer Malignancy Models from Chest X-ray Images
Decision trees mined using this method may be considered as a starting point for refinement into clinically useful multi-variate lung cancer malignancy models for implementation as a workflow augmentation tool to improve the efficiency of human radiologists.
