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
A systematic approach to deep learning-based nodule detection in chest radiographs
FinnBehrendt/node21-submit
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Nature Scientific Reports 2023
We illustrate how this analysis and a combination of multiple architectures results in state-of-the-art performance for lung nodule detection, which is demonstrated by the proposed model winning the detection track of the Node21 competition.
Specialty-Oriented Generalist Medical AI for Chest CT Screening
niuchuangnn/m3fm
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Modern medical records include a vast amount of multimodal free text clinical data and imaging data from radiology, cardiology, and digital pathology.
SCPM-Net: An Anchor-free 3D Lung Nodule Detection Network using Sphere Representation and Center Points Matching
To overcome these problems, we propose a 3D sphere representation-based center-points matching detection network that is anchor-free and automatically predicts the position, radius, and offset of nodules without the manual design of nodule/anchor parameters.
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.
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.
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.
DeepSEED: 3D Squeeze-and-Excitation Encoder-Decoder Convolutional Neural Networks for Pulmonary Nodule Detection
ymli39/DeepSEED-3D-ConvNets-for-Pulmonary-Nodule-Detection
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Pulmonary nodule detection plays an important role in lung cancer screening with low-dose computed tomography (CT) scans.
DeepEM: Deep 3D ConvNets With EM For Weakly Supervised Pulmonary Nodule Detection
wentaozhu/DeepEM-for-Weakly-Supervised-Detection
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Recently deep learning has been witnessing widespread adoption in various medical image applications.
A Systematic Analysis for State-of-the-Art 3D Lung Nodule Proposals Generation
Lung nodule proposals generation is the primary step of lung nodule detection and has received much attention in recent years .
