Lung Nodule Segmentation
12 papers with code • 5 benchmarks • 2 datasets
Benchmarks
These leaderboards are used to track progress in Lung Nodule Segmentation
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Libraries
Use these libraries to find Lung Nodule Segmentation models and implementations
LIDC-IDRI
LUNA
Latest papers with no code
Full-resolution Lung Nodule Segmentation from Chest X-ray Images using Residual Encoder-Decoder Networks
The proposed algorithm achieved excellent generalization results against an external dataset with sensitivity of 77% at a false positive rate of 7. 6.
MESAHA-Net: Multi-Encoders based Self-Adaptive Hard Attention Network with Maximum Intensity Projections for Lung Nodule Segmentation in CT Scan
By employing a novel adaptive hard attention mechanism, MESAHA-Net iteratively performs slice-by-slice 2D segmentation of lung nodules, focusing on the nodule region in each slice to generate 3D volumetric segmentation of lung nodules.
Lung Nodule Segmentation and Uncertain Region Prediction with an Uncertainty-Aware Attention Mechanism
Radiologists possess diverse training and clinical experiences, leading to variations in the segmentation annotations of lung nodules and resulting in segmentation uncertainty. Conventional methods typically select a single annotation as the learning target or attempt to learn a latent space comprising multiple annotations.
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.
Accurate Lung Nodules Segmentation with Detailed Representation Transfer and Soft Mask Supervision
Then, a novel Network with detailed representation transfer and Soft Mask supervision (DSNet) is proposed to process the input low-resolution images of lung nodules into high-quality segmentation results.
Volumetric Lung Nodule Segmentation using Adaptive ROI with Multi-View Residual Learning
The proposed technique can be segregated into two stages, at the first stage, it takes a 2-D ROI containing the nodule as input and it performs patch-wise investigation along the axial axis with a novel adaptive ROI strategy.
Dual-branch residual network for lung nodule segmentation
Experimental results show that the DB-ResNet achieves superior segmentation performance with an average dice score of 82. 74% on the dataset.
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.
Filter sharing: Efficient learning of parameters for volumetric convolutions
Typical convolutional neural networks (CNNs) have several millions of parameters and require a large amount of annotated data to train them.
