AIMC Topic: Lung Neoplasms
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MVCNet: Multiview Contrastive Network for Unsupervised Representation Learning for 3-D CT Lesions.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
With the renaissance of deep learning, automatic diagnostic algorithms for computed tomography (CT) have achieved many successful applications. However, they heavily rely on lesion-level annotations, which are often scarce due to the high cost of col...
GMILT: A Novel Transformer Network That Can Noninvasively Predict EGFR Mutation Status.
IEEE transactions on neural networks and learning systems
Jun 3, 2024
Noninvasively and accurately predicting the epidermal growth factor receptor (EGFR) mutation status is a clinically vital problem. Moreover, further identifying the most suspicious area related to the EGFR mutation status can guide the biopsy to avoi...
The utility of artificial intelligence in identifying radiological evidence of lung cancer and pulmonary tuberculosis in a high-burden tuberculosis setting.
South African medical journal = Suid-Afrikaanse tydskrif vir geneeskunde
May 31, 2024
BACKGROUND: Artificial intelligence (AI), using deep learning (DL) systems, can be utilised to detect radiological changes of various pulmonary diseases. Settings with a high burden of tuberculosis (TB) and people living with HIV can potentially bene...
Coronary Artery Calcification on Low-Dose Lung Cancer Screening CT in South Korea: Visual and Artificial Intelligence-Based Assessment and Association With Cardiovascular Events.
AJR. American journal of roentgenology
May 29, 2024
Coronary artery calcification (CAC) on lung cancer screening low-dose chest CT (LDCT) is a cardiovascular risk marker. South Korea was the first Asian country to initiate a national LDCT lung cancer screening program, although CAC-related outcomes a...
Artificial Intelligence: Can It Save Lives, Hospitals, and Lung Screening?
The Annals of thoracic surgery
May 28, 2024
BACKGROUND: Early detection is essential in lung cancer survival. Lung screening or incidental detection on unrelated imaging holds the most promise for early detection. With the large volume of imaging performed today, management of incidental pulmo...
Value of CT-Based Deep Learning Model in Differentiating Benign and Malignant Solid Pulmonary Nodules ≤ 8 mm.
Academic radiology
May 27, 2024
RATIONALE AND OBJECTIVES: We examined the effectiveness of computed tomography (CT)-based deep learning (DL) models in differentiating benign and malignant solid pulmonary nodules (SPNs) ≤ 8 mm.
Optimizing double-layered convolutional neural networks for efficient lung cancer classification through hyperparameter optimization and advanced image pre-processing techniques.
BMC medical informatics and decision making
May 27, 2024
Lung cancer remains a leading cause of cancer-related mortality globally, with prognosis significantly dependent on early-stage detection. Traditional diagnostic methods, though effective, often face challenges regarding accuracy, early detection, an...
Systematic review and meta-analysis of deep learning applications in computed tomography lung cancer segmentation.
Radiotherapy and oncology : journal of the European Society for Therapeutic Radiology and Oncology
May 26, 2024
BACKGROUND: Accurate segmentation of lung tumors on chest computed tomography (CT) scans is crucial for effective diagnosis and treatment planning. Deep Learning (DL) has emerged as a promising tool in medical imaging, particularly for lung cancer se...
A deep learning-based radiomics model for predicting lymph node status from lung adenocarcinoma.
BMC medical imaging
May 24, 2024
OBJECTIVES: At present, there are many limitations in the evaluation of lymph node metastasis of lung adenocarcinoma. Currently, there is a demand for a safe and accurate method to predict lymph node metastasis of lung cancer. In this study, radiomic...
Preoperative evaluation of visceral pleural invasion in peripheral lung cancer utilizing deep learning technology.
Surgery today
May 23, 2024
PURPOSE: This study aimed to assess the efficiency of artificial intelligence (AI) in the detection of visceral pleural invasion (VPI) of lung cancer using high-resolution computed tomography (HRCT) images, which is challenging for experts because of...
Deep Learning
Lung Neoplasms
Carcinoma, Non-Small-Cell Lung
Neoplasm Invasiveness
Sensitivity and Specificity
Male
Middle Aged
Neoplasm Staging
Pleural Neoplasms
Preoperative Period
Pleura
Humans
Artificial Intelligence
Retrospective Studies
Tomography, X-Ray Computed
Female
Aged
Aged, 80 and over
Adult
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