Lung Nodule Classification
10 papers with code • 1 benchmarks • 1 datasets
LIDC-IDRI
Most implemented papers
DeepLung: Deep 3D Dual Path Nets for Automated Pulmonary Nodule Detection and Classification
DeepLung consists of two components, nodule detection (identifying the locations of candidate nodules) and classification (classifying candidate nodules into benign or malignant).
Diagnostic Classification Of Lung Nodules Using 3D Neural Networks
Lung cancer is the leading cause of cancer-related death worldwide.
Fast CapsNet for Lung Cancer Screening
We show that CapsNets significantly outperforms CNNs when the number of training samples is small.
Multi-level CNN for lung nodule classification with Gaussian Process assisted hyperparameter optimization
In this paper, a non-stationary kernel is proposed which allows the surrogate model to adapt to functions whose smoothness varies with the spatial location of inputs, and a multi-level convolutional neural network (ML-CNN) is built for lung nodule classification whose hyperparameter configuration is optimized by using the proposed non-stationary kernel based Gaussian surrogate model.
Lung Nodule Classification using Deep Local-Global Networks
In this paper, we propose a novel method to predict the malignancy of nodules that have the capability to analyze the shape and size of a nodule using a global feature extractor, as well as the density and structure of the nodule using a local feature extractor.
Lung nodule detection and classification from Thorax CT-scan using RetinaNet with transfer learning
ivanwilliammd/I3DR-Net-Transfer-Learning
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Journal of King Saud University - Computer and Information Sciences 2020
Lung malignancy is one of the most common causes of death in the world caused by malignant lung nodules which commonly diagnosed radiologically by radiologists.
Learning Efficient, Explainable and Discriminative Representations for Pulmonary Nodules Classification
fei-hdu/NAS-Lung
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Besides, empirical study shows that the reasoning process of learned networks is in conformity with physicians' diagnosis.
Lung cancer detection from thoracic CT scans using an ensemble of deep learning models
Our approach leverages deep transfer learning and adopts an ensemble learning approach.
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.
Medical Slice Transformer: Improved Diagnosis and Explainability on 3D Medical Images with DINOv2
We introduce the Medical Slice Transformer (MST) framework to adapt 2D self-supervised models for 3D medical image analysis.
