. Monte Carlo (MC) simulation remains the gold standard for modeling complex physical interactions in transmission and emission tomography, with graphic processing unit (GPU) parallel computing offering unmatched computational performance and enablin...
. Neurite orientation dispersion and density imaging (NODDI) microstructure estimation from diffusion magnetic resonance imaging (dMRI) is of great significance for the discovery and treatment of various neurological diseases. Current deep learning-b...
It is well known that machine learning models require a high amount of annotated data to obtain optimal performance. Labelling Computed Tomography (CT) data can be a particularly challenging task due to its volumetric nature and often missing and/or ...
Unilateral peripheral facial palsy (PFP) results in facial asymmetry and functional impairment, reducing quality of life. Accurate, objective assessment is vital for monitoring and rehabilitation. This study presents an automated method utilizes stan...
Hyperspectral imaging has shown significant applicability in the medical field, particularly for its ability to represent spectral information that can differentiate specific biomolecular characteristics in tissue samples. However, the complexity of ...
Brain tumor classification (BTC) from Magnetic Resonance Imaging (MRI) is a critical diagnosis task, which is highly important for treatment planning. In this study, we propose a hybrid deep learning (DL) model that integrates VGG16, an attention mec...
Self-supervised learning (SSL) has gained significant attention in medical imaging for its ability to leverage large amounts of unlabeled data for effective model pretraining. Among SSL methods, the masked autoencoder (MAE) has proven robust in learn...
This paper explores the application of deep learning (DL) techniques in landscape design and plant selection, aiming to enhance design efficiency and quality through automated plant leaf image recognition (PLIR). A novel framework based on Convolutio...
Early and accurate brain tumor classification is vital for clinical diagnosis and treatment. Although Convolutional Neural Networks (CNNs) are widely used in medical image analysis, they often struggle to focus on critical information adequately and ...
The timely and precise identification of diseases in plants is essential for efficient disease control and safeguarding of crops. Manual identification of diseases requires expert knowledge in the field, and finding people with domain knowledge is ch...
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