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
INCEPTNET: Precise And Early Disease Detection Application For Medical Images Analyses
In this study, we propose a novel deep neural network (DNN), entitled InceptNet, in the scope of medical image processing, for early disease detection and segmentation of medical images in order to enhance precision and performance.
AC-Norm: Effective Tuning for Medical Image Analysis via Affine Collaborative Normalization
endoluminalsurgicalvision-imr/acnorm
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Driven by the latest trend towards self-supervised learning (SSL), the paradigm of "pretraining-then-finetuning" has been extensively explored to enhance the performance of clinical applications with limited annotations.
Probabilistic 3D segmentation for aleatoric uncertainty quantification in full 3D medical data
cviviers/prob_3d_segmentation
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To this end, we have developed a 3D probabilistic segmentation framework augmented with NFs, to enable capturing the distributions of various complexity.
Uncertainty-Guided Lung Nodule Segmentation with Feature-Aware Attention
With an Uncertainty-Aware Module, this network can provide a Multi-Confidence Mask (MCM), pointing out regions with different segmentation uncertainty levels.
Learning Semantics-enriched Representation via Self-discovery, Self-classification, and Self-restoration
To this end, we train deep models to learn semantically enriched visual representation by self-discovery, self-classification, and self-restoration of the anatomy underneath medical images, resulting in a semantics-enriched, general-purpose, pre-trained 3D model, named Semantic Genesis.
U-Det: A Modified U-Net architecture with bidirectional feature network for lung nodule segmentation
NikV-JS/U-Det
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Early diagnosis and analysis of lung cancer involve a precise and efficient lung nodule segmentation in computed tomography (CT) images.
Level set image segmentation with velocity term learned from data with applications to lung nodule segmentation
Approach: We introduce an extension of the standard level set image segmentation method where the velocity function is learned from data via machine learning regression methods, rather than a priori designed.
Bi-Directional ConvLSTM U-Net with Densley Connected Convolutions
rezazad68/BCDU-Net
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In Proceedings of the IEEE/CVF international conference on computer vision workshops 2019
To strengthen feature propagation and encourage feature reuse, we use densely connected convolutions in the last convolutional layer of the encoding path.
Models Genesis: Generic Autodidactic Models for 3D Medical Image Analysis
More importantly, learning a model from scratch simply in 3D may not necessarily yield performance better than transfer learning from ImageNet in 2D, but our Models Genesis consistently top any 2D approaches including fine-tuning the models pre-trained from ImageNet as well as fine-tuning the 2D versions of our Models Genesis, confirming the importance of 3D anatomical information and significance of our Models Genesis for 3D medical imaging.
iW-Net: an automatic and minimalistic interactive lung nodule segmentation deep network
We propose iW-Net, a deep learning model that allows for both automatic and interactive segmentation of lung nodules in computed tomography images.
