Lung Nodule Classification
8 papers with code • 1 benchmarks • 1 datasets
Benchmarks
These leaderboards are used to track progress in Lung Nodule Classification
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LIDC-IDRI
Latest papers with no code
ProCAN: Progressive Growing Channel Attentive Non-Local Network for Lung Nodule Classification
Nevertheless, the large variation in the size and heterogeneous appearance of the nodules makes this task an extremely challenging one.
Lung Nodule Classification Using Biomarkers, Volumetric Radiomics and 3D CNNs
Our algorithm employs a 3D Convolutional Neural Network (CNN) as well as a Random Forest in order to combine CT imagery with biomarker annotation and volumetric radiomic features.
Contraction Mapping of Feature Norms for Classifier Learning on the Data with Different Quality
The experiments on various classification applications, including handwritten digit recognition, lung nodule classification, face verification and face recognition, demonstrate that the proposed approach is promising to effectively deal with the problem of learning on the data with different quality and leads to the significant and stable improvements in the classification accuracy.
Radiomic feature selection for lung cancer classifiers
In this study, we investigate the impact of supervised and unsupervised feature selection techniques on machine learning methods for nodule classification in Computed Tomography (CT) images.
Gated-Dilated Networks for Lung Nodule Classification in CT scans
Different types of Convolutional Neural Networks (CNNs) have been applied to detect cancerous lung nodules from computed tomography (CT) scans.
Shape and Margin-Aware Lung Nodule Classification in Low-dose CT Images via Soft Activation Mapping
Therefore, CAM and Grad-CAM cannot provide optimal interpretation for lung nodule categorization task in low-dose CT images, in that fine-grained pathological clues like discrete and irregular shape and margins of nodules are capable of enhancing sensitivity and specificity of nodule classification with regards to CNN.
Classification of lung nodules in CT images based on Wasserstein distance in differential geometry
The Wasserstein distance between the nodules is calculated based on our new spherical optimal mass transport, this new algorithm works directly on sphere by using spherical metric, which is much more accurate and efficient than previous methods.
Joint Learning for Pulmonary Nodule Segmentation, Attributes and Malignancy Prediction
Refer to the literature of lung nodule classification, many studies adopt Convolutional Neural Networks (CNN) to directly predict the malignancy of lung nodules with original thoracic Computed Tomography (CT) and nodule location.
Lung Nodule Classification by the Combination of Fusion Classifier and Cascaded Convolutional Neural Networks
In this paper, we propose Fusion classifier in conjunction with the cascaded convolutional neural network models.
Lung Cancer Screening Using Adaptive Memory-Augmented Recurrent Networks
In this paper, we investigate the effectiveness of deep learning techniques for lung nodule classification in computed tomography scans.
