AIMC Topic: Solitary Pulmonary Nodule
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OMS-CNN: Optimized Multi-Scale CNN for Lung Nodule Detection Based on Faster R-CNN.
IEEE journal of biomedical and health informatics
Mar 6, 2025
The global increase in lung cancer cases, often marked by pulmonary nodules, underscores the critical importance of timely detection to mitigate cancer progression and reduce morbidity and mortality. The Faster R-CNN approach is a two-stage, high-pre...
Assessments of lung nodules by an artificial intelligence chatbot using longitudinal CT images.
Cell reports. Medicine
Mar 4, 2025
Large language models have shown efficacy across multiple medical tasks. However, their value in the assessment of longitudinal follow-up computed tomography (CT) images of patients with lung nodules is unclear. In this study, we evaluate the ability...
Predicting malignant risk of ground-glass nodules using convolutional neural networks based on dual-time-point F-FDG PET/CT.
Cancer imaging : the official publication of the International Cancer Imaging Society
Feb 18, 2025
BACKGROUND: Accurately predicting the malignant risk of ground-glass nodules (GGOs) is crucial for precise treatment planning. This study aims to utilize convolutional neural networks based on dual-time-point F-FDG PET/CT to predict the malignant ris...
Prior knowledge-based multi-task learning network for pulmonary nodule classification.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Feb 16, 2025
The morphological characteristics of pulmonary nodule, also known as the attributes, are crucial for classification of benign and malignant nodules. In clinical, radiologists usually conduct a comprehensive analysis of correlations between different ...
A Novel Deep Learning-Based (3D U-Net Model) Automated Pulmonary Nodule Detection Tool for CT Imaging.
Current oncology (Toronto, Ont.)
Feb 8, 2025
BACKGROUND: Precise detection and characterization of pulmonary nodules on computed tomography (CT) is crucial for early diagnosis and management.
Explainable Classification of Benign-Malignant Pulmonary Nodules With Neural Networks and Information Bottleneck.
IEEE transactions on neural networks and learning systems
Feb 6, 2025
Computerized tomography (CT) is a clinically primary technique to differentiate benign-malignant pulmonary nodules for lung cancer diagnosis. Early classification of pulmonary nodules is essential to slow down the degenerative process and reduce mort...
Cross-ViT based benign and malignant classification of pulmonary nodules.
PloS one
Feb 5, 2025
The benign and malignant discrimination of pulmonary nodules plays a very important role in diagnosing the extent of lung cancer lesions. There are many methods using Convolutional neural network (CNN) for benign and malignant classification of pulmo...
Impact of Deep Learning 3D CT Super-Resolution on AI-Based Pulmonary Nodule Characterization.
Tomography (Ann Arbor, Mich.)
Jan 27, 2025
BACKGROUND/OBJECTIVES: Correct pulmonary nodule volumetry and categorization is paramount for accurate diagnosis in lung cancer screening programs. CT scanners with slice thicknesses of multiple millimetres are still common worldwide, and slice thick...
Deep-learning based electromagnetic navigation system for transthoracic percutaneous puncture of small pulmonary nodules.
Scientific reports
Jan 20, 2025
Percutaneous transthoracic puncture of small pulmonary nodules is technically challenging. We developed a novel electromagnetic navigation puncture system for the puncture of sub-centimeter lung nodules by combining multiple deep learning models with...
Using natural language processing to identify emergency department patients with incidental lung nodules requiring follow-up.
Academic emergency medicine : official journal of the Society for Academic Emergency Medicine
Jan 17, 2025
OBJECTIVES: For emergency department (ED) patients, lung cancer may be detected early through incidental lung nodules (ILNs) discovered on chest CTs. However, there are significant errors in the communication and follow-up of incidental findings on E...
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