Results 31 to 40 of about 1,366,030 (170)

Regression to Classification: Ordinal Prediction of Calcified Vessels Using Customized ResNet50

IEEE Access, 2023
A substantial percentage of women die from cardiovascular disease (CVD). Computed tomography (CT) scan helps predict/monitor CVD-related diseases. However, previous studies found relation to assessing the risk of CVD by estimating severity of breast ...
Hosna Asma-Ull, Il Dong Yun, Bo La Yun
doaj   +1 more source

Point-Sampling Method Based on 3D U-Net Architecture to Reduce the Influence of False Positive and Solve Boundary Blur Problem in 3D CT Image Segmentation

Applied Sciences, 2020
Malignant lesions are a huge threat to human health and have a high mortality rate. Locating the contour of organs is a preparation step, and it helps doctors diagnose correctly.
Chen Li, Wei Chen, Yusong Tan
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Pharmacokinetic modelling of MRI-based liver function for risk assessment in primary sclerosing cholangitis: a prospective pilot study

European Radiology Experimental
Objective Primary sclerosing cholangitis (PSC) is a rare fibroinflammatory hepatobiliary disease with a highly variable clinical course. Identifying patients at risk for poor outcomes remains challenging. Magnetic resonance imaging (MRI)-based approaches
Wolf C. Bartholomä, Shan Cai, Christian Simonsson, Markus Karlsson, Stergios Kechagias, Mischa Woisetschläger, Nils Dahlström, Peter Lundberg   +7 more
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Deep Learning Applications in Medical Image Processing

Acta Infologica, 2021
Medical imaging techniques such as magnetic resonance imaging (MRI), computed tomography (CT), positron emission tomography (PET), mammography, ultrasound and x-ray have been used for the diagnosis and treatment of diseases for many years.
Ayşe Gül Eker, Nevcihan Duru
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The potential of federated learning for self-configuring medical object detection in heterogeneous data distributions

Scientific Reports
Medical Object Detection (MOD) is a clinically relevant image processing method that locates structures of interest in radiological image data at object-level using bounding boxes.
Gabriel Rashidi, Dimitrios Bounias, Markus Bujotzek, Andrés Martínez Mora, Peter Neher, Klaus H. Maier-Hein   +5 more
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Deep learning methods for noisy sperm image classification from convolutional neural network to visual transformer: a comprehensive comparative study

Intelligent Medicine
Background With the gradual increase of infertility in the world, among which male sperm problems are the main factor for infertility, more and more couples are using computer-assisted sperm analysis (CASA) to assist in the analysis and treatment of ...
Ao Chen, Chen Li, Md Mamunur Rahaman, Yudong Yao, Haoyuan Chen, Hechen Yang, Peng Zhao, Weiming Hu, Wanli Liu, Shuojia Zou, Ning Xu, Marcin Grzegorzek   +11 more
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Evaluating the Efficacy of Deep Learning Models for Identifying Manipulated Medical Fundus Images

AI
(1) Background: The misuse of transformation technology using medical images is a critical problem that can endanger patients’ lives, and detecting manipulation via a deep learning model is essential to address issues of manipulated medical images that ...
Ho-Jung Song, Ju-Hyuck Han, You-Sang Cho, Yong-Suk Kim   +3 more
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Segmentation model of soft tissue sarcoma based on self-supervised learning

Frontiers in Oncology
IntroductionSoft tissue sarcomas, similar in incidence to cervical and esophageal cancers, arise from various soft tissues like smooth muscle, fat, and fibrous tissue.
Minting Zheng, Minting Zheng, Chenhua Guo, Yifeng Zhu, Xiaoming Gang, Chongyang Fu, Shaowu Wang   +6 more
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A Novel Artificial-Intelligence-Based Approach for Automatic Assessment of Retinal Disease Images Using Multi-View Deep-Broad Learning Network

IEEE Access
Retinal disease detection and diagnosis relying solely on artificial retinal diseases will put great pressure on doctors and increase the rate of misdiagnosis.
Ao Zhang, Xin Qian, Chengcheng Xu, Jie Zhang   +3 more
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Measuring CSF net velocity in the human subarachnoid space with 7T MRI: testing the classical view of CSF absorption

Fluids and Barriers of the CNS
Traditionally, cerebrospinal fluid (CSF) is believed to exit the brain via arachnoid villi, being absorbed into the superior sagittal sinus (SSS), with a net flow towards these exit sites driven by constant CSF turnover.
E. C. van der Voort, M. C. E. van der Plas, M. W. J. M. Gosselink, J. J. M. Zwanenburg   +3 more
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