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Tomography, X-Ray Computed

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[Comparison of the Impact of Deep Learning Techniques on Low-noise Head CT Images].

Nihon Hoshasen Gijutsu Gakkai zasshi
PURPOSE: This study aims to compare the effects of two types of deep learning (DL) techniques on brain CT values, image noise content, and contrast-to-noise ratio (CNR) between white and gray matter in low-noise head CT images, along with adaptive it...

An Early Thyroid Screening Model Based on Transformer and Secondary Transfer Learning for Chest and Thyroid CT Images.

Technology in cancer research & treatment
IntroductionThyroid cancer is a common malignant tumor, and early diagnosis and timely treatment are crucial to improve patient prognosis. With the increasing use of enhanced CT scans, a new opportunity for early thyroid cancer screening has emerged....

A vision transformer-convolutional neural network framework for decision-transparent dual-energy X-ray absorptiometry recommendations using chest low-dose CT.

International journal of medical informatics
OBJECTIVE: This study introduces an ensemble framework that integrates Vision Transformer (ViT) and Convolutional Neural Networks (CNN) models to leverage their complementary strengths, generating visualized and decision-transparent recommendations f...

Predicting strength of femora with metastatic lesions from single 2D radiographic projections using convolutional neural networks.

Computer methods and programs in biomedicine
BACKGROUND AND OBJECTIVE: Patients with metastatic bone disease are at risk of pathological femoral fractures and may require prophylactic surgical fixation. Current clinical decision support tools often overestimate fracture risk, leading to overtre...

Predicting hepatocellular carcinoma response to TACE: A machine learning study based on 2.5D CT imaging and deep features analysis.

European journal of radiology
OBJECTIVES: Prior to the commencement of treatment, it is essential to establish an objective method for accurately predicting the prognosis of patients with hepatocellular carcinoma (HCC) undergoing transarterial chemoembolization (TACE). In this st...

Lung cancer detection and classification using optimized CNN features and Squeeze-Inception-ResNeXt model.

Computational biology and chemistry
Lung cancer, with its high mortality rate, is one of the deadliest diseases globally. The alarming increase in lung cancer deaths and its widespread prevalence have led to the development of various cancer control research and early detection methods...

A Neural Network Model for Intelligent Classification of Distal Radius Fractures Using Statistical Shape Model Extraction Features.

Orthopaedic surgery
OBJECTIVE: Distal radius fractures account for 12%-17% of all fractures, with accurate classification being crucial for proper treatment planning. Studies have shown that in emergency settings, the misdiagnosis rate of hand/wrist fractures can reach ...

Large Language Models in Summarizing Radiology Report Impressions for Lung Cancer in Chinese: Evaluation Study.

Journal of medical Internet research
BACKGROUND: Large language models (LLMs), such as ChatGPT, have demonstrated impressive capabilities in various natural language processing tasks, particularly in text generation. However, their effectiveness in summarizing radiology report impressio...

Evaluating the dosimetric and positioning accuracy of a deep learning based synthetic-CT model for liver radiotherapy treatment planning.

Biomedical physics & engineering express
An MRI-only workflow requires synthetic computed tomography (sCT) images to enable dose calculation. This study evaluated the dosimetric and patient positioning accuracy of deep learning-generated sCT for liver radiotherapy.sCT images were generated ...

Clinically applicable semi-supervised learning framework for multiple organs at risk and tumor delineation in lung cancer brachytherapy.

Physica medica : PM : an international journal devoted to the applications of physics to medicine and biology : official journal of the Italian Association of Biomedical Physics (AIFB)
PURPOSE: The generalization ability of deep learning-based automatic segmentation techniques for lung cancer in practical clinical applications remains under-validated. We reported an investigation that validated a robust semi-supervised conditional ...