AIMC Topic: Tomography, X-Ray Computed
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Light&fast generative adversarial network for high-fidelity CT image synthesis of liver tumor.
Computer methods and programs in biomedicine
May 28, 2024
BACKGROUND AND OBJECTIVE: Hepatocellular carcinoma is a common disease with high mortality. Through deep learning methods to analyze HCC CT, the screening classification and prognosis model of HCC can be established, which further promotes the develo...
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...
Deep learning for risk stratification of thymoma pathological subtypes based on preoperative CT images.
BMC cancer
May 28, 2024
OBJECTIVES: This study aims to develop an innovative, deep model for thymoma risk stratification using preoperative CT images. Current algorithms predominantly focus on radiomic features or 2D deep features and require manual tumor segmentation by ra...
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...
Fog-based deep learning framework for real-time pandemic screening in smart cities from multi-site tomographies.
BMC medical imaging
May 27, 2024
The quick proliferation of pandemic diseases has been imposing many concerns on the international health infrastructure. To combat pandemic diseases in smart cities, Artificial Intelligence of Things (AIoT) technology, based on the integration of art...
Deep learning nomogram for predicting neoadjuvant chemotherapy response in locally advanced gastric cancer patients.
Abdominal radiology (New York)
May 26, 2024
PURPOSE: Developed and validated a deep learning radiomics nomogram using multi-phase contrast-enhanced computed tomography (CECT) images to predict neoadjuvant chemotherapy (NAC) response in locally advanced gastric cancer (LAGC) patients.
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...
Deep-learning segmentation to select liver parenchyma for categorizing hepatic steatosis on multinational chest CT.
Scientific reports
May 25, 2024
Unenhanced CT scans exhibit high specificity in detecting moderate-to-severe hepatic steatosis. Even though many CTs are scanned from health screening and various diagnostic contexts, their potential for hepatic steatosis detection has largely remain...
A comparative analysis of different augmentations for brain images.
Medical & biological engineering & computing
May 24, 2024
Deep learning (DL) requires a large amount of training data to improve performance and prevent overfitting. To overcome these difficulties, we need to increase the size of the training dataset. This can be done by augmentation on a small dataset. The...
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