AIMC Topic: Radiographic Image Interpretation, Computer-Assisted
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Clinical application of convolutional neural network lung nodule detection software: An Australian quaternary hospital experience.
Journal of medical imaging and radiation oncology
Aug 9, 2024
INTRODUCTION: Early-stage lung cancer diagnosis through detection of nodules on computed tomography (CT) remains integral to patient survivorship, promoting national screening programmes and diagnostic tools using artificial intelligence (AI) convolu...
CMM: A CNN-MLP Model for COVID-19 Lesion Segmentation and Severity Grading.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 9, 2024
In this paper, a CNN-MLP model (CMM) is proposed for COVID-19 lesion segmentation and severity grading in CT images. The CMM starts by lung segmentation using UNet, and then segmenting the lesion from the lung region using a multi-scale deep supervis...
Automated 3D Cobb Angle Measurement Using U-Net in CT Images of Preoperative Scoliosis Patients.
Journal of imaging informatics in medicine
Aug 8, 2024
To propose a deep learning framework "SpineCurve-net" for automated measuring the 3D Cobb angles from computed tomography (CT) images of presurgical scoliosis patients. A total of 116 scoliosis patients were analyzed, divided into a training set of 8...
Detection of Lungs Tumors in CT Scan Images Using Convolutional Neural Networks.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Current human being's lifestyle has caused / exacerbated many diseases. One of these diseases is cancer, and among all kinds of cancers like, brain pulmonary; lung cancer is fatal. The cancers could be detected early to save lives using Computer Aide...
Explainable Knowledge Distillation for On-Device Chest X-Ray Classification.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Automated multi-label chest X-rays (CXR) image classification has achieved substantial progress in clinical diagnosis via utilizing sophisticated deep learning approaches. However, most deep models have high computational demands, which makes them le...
CDT-CAD: Context-Aware Deformable Transformers for End-to-End Chest Abnormality Detection on X-Ray Images.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Deep learning methods have achieved great success in medical image analysis domain. However, most of them suffer from slow convergency and high computing cost, which prevents their further widely usage in practical scenarios. Moreover, it has been pr...
A Deep Learning Method for Pneumonia Detection Based on Fuzzy Non-Maximum Suppression.
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Pneumonia is one of the largest causes of death in the world. Deep learning techniques can assist doctors to detect the areas of pneumonia in the chest X-rays images. However, existing methods lack sufficient consideration for the large variation sca...
A Deep Learning Approach Considering Image Background for Pneumonia Identification Using Explainable AI (XAI).
IEEE/ACM transactions on computational biology and bioinformatics
Aug 8, 2024
Pneumonia mainly refers to lung infections caused by pathogens, such as bacteria and viruses. Currently, deep learning methods have been applied to identify pneumonia. However, the traditional deep learning methods for pneumonia identification take l...
Ensemble machine learning to predict futile recanalization after mechanical thrombectomy based on non-contrast CT imaging.
Journal of stroke and cerebrovascular diseases : the official journal of National Stroke Association
Aug 6, 2024
OBJECTIVES: Despite successful recanalization after Mechanical Thrombectomy (MT), approximately 25 % of patients with Acute Ischemic Stroke (AIS) due to Large Vessel Occlusion (LVO) show unfavorable clinical outcomes, namely Futile Recanalization (FR...
Humans
Machine Learning
Decision Support Techniques
Thrombectomy
Medical Futility
Ischemic Stroke
Radiographic Image Interpretation, Computer-Assisted
Infarction, Middle Cerebral Artery
Aged, 80 and over
Male
Middle Aged
Female
Treatment Outcome
Risk Factors
Predictive Value of Tests
Aged
Risk Assessment
Retrospective Studies
Tomography, X-Ray Computed
Time Factors
Clinical Decision-Making
Precision and Robust Models on Healthcare Institution Federated Learning for Predicting HCC on Portal Venous CT Images.
IEEE journal of biomedical and health informatics
Aug 6, 2024
Hepatocellular carcinoma (HCC), the most common type of liver cancer, poses significant challenges in detection and diagnosis. Medical imaging, especially computed tomography (CT), is pivotal in non-invasively identifying this disease, requiring subs...
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