Journal of Imaging Informatics in Medicine
SCIE-ISI
2948-2933
Cơ quản chủ quản: N/A
Phân tích ảnh hưởng
Các bài báo tiêu biểu
MRI-Based Machine Learning Fusion Models to Distinguish Encephalitis and Gliomas
AbstractThis paper aims to compare the performance of the classical machine
learning (CML) model and the deep learning (DL) model, and to assess the
effectiveness of utilizing fusion radiomics from both CML and DL in
distinguishing encephalitis from glioma in atypical cases. We analysed the axial
FLAIR images of preoperative MRI in 116 patients pathologically confirmed as
gliomas and clinically di...
Invertible and Variable Augmented Network for Pretreatment Patient-Specific Quality Assurance Dose Prediction
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Optimizing Coronary Computed Tomography Angiography Using a Novel Deep Learning-Based Algorithm
AbstractCoronary computed tomography angiography (CCTA) is an essential part of
the diagnosis of chronic coronary syndrome (CCS) in patients with
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64-row multidetector CT (64-MDCT), which is still frequently used, although it
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z-coverage. In this ...
CT-Based Intratumoral and Peritumoral Radiomics Nomograms for the Preoperative Prediction of Spread Through Air Spaces in Clinical Stage IA Non-small Cell Lung Cancer
AbstractThe study aims to investigate the value of intratumoral and peritumoral
radiomics and clinical-radiological features for predicting spread through air
spaces (STAS) in patients with clinical stage IA non-small cell lung cancer
(NSCLC). A total of 336 NSCLC patients from our hospital were randomly divided
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Deep Learning for Automated Detection and Localization of Traumatic Abdominal Solid Organ Injuries on CT Scans
AbstractComputed tomography (CT) is the most commonly used diagnostic modality
for blunt abdominal trauma (BAT), significantly influencing management
approaches. Deep learning models (DLMs) have shown great promise in enhancing
various aspects of clinical practice. There is limited literature available on
the use of DLMs specifically for trauma image evaluation. In this study, we
developed a DLM a...