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 with no code
Improved Focus on Hard Samples for Lung Nodule Detection
Recently, lung nodule detection methods based on deep learning have shown excellent performance in the medical image processing field.
EXACT-Net:EHR-guided lung tumor auto-segmentation for non-small cell lung cancer radiotherapy
Lung cancer is a devastating disease with the highest mortality rate among cancer types.
Swin-Tempo: Temporal-Aware Lung Nodule Detection in CT Scans as Video Sequences Using Swin Transformer-Enhanced UNet
However, identifying lung nodules poses significant challenges for radiologists, who rely heavily on their expertise for accurate diagnosis.
An Efficient and Robust Method for Chest X-Ray Rib Suppression that Improves Pulmonary Abnormality Diagnosis
Suppression of thoracic bone shadows on chest X-rays (CXRs) has been indicated to improve the diagnosis of pulmonary disease.
Joint Self-Supervised Image-Volume Representation Learning with Intra-Inter Contrastive Clustering
Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data.
MEDS-Net: Self-Distilled Multi-Encoders Network with Bi-Direction Maximum Intensity projections for Lung Nodule Detection
Particularly, we exploit Bi-Directional Maximum intensity projection (MIP) images of various thicknesses (i. e., 3, 5 and 10mm) along with a 3D patch of CT scan, consisting of 10 adjacent slices to feed into self-distillation-based Multi-Encoders Network (MEDS-Net).
Image Synthesis with Disentangled Attributes for Chest X-Ray Nodule Augmentation and Detection
Moreover, we propose to synthesize nodule CXR images by controlling the disentangled nodule attributes for data augmentation, in order to better compensate for the nodules that are easily missed in the detection task.
Multi-Task Lung Nodule Detection in Chest Radiographs with a Dual Head Network
In this work, we present a multi-task lung nodule detection algorithm for chest radiograph analysis.
AI-based software for lung nodule detection in chest X-rays -- Time for a second reader approach?
Conclusions: Both AI modes flagged the pulmonary nodules missed by radiologists in a significant number of cases.
Unsupervised Contrastive Learning based Transformer for Lung Nodule Detection
To effectively train the transformer model on a relatively small dataset, the region-based contrastive learning method is used to boost the performance by pre-training the 3D transformer with public CT images.
