AIMC Topic: Image Processing, Computer-Assisted

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A hybrid deep learning model approach for automated detection and classification of cassava leaf diseases.

Scientific reports Feb 27, 2025
Detecting cassava leaf disease is challenging because it is hard to identify diseases accurately through visual inspection. Even trained agricultural experts may struggle to diagnose the disease correctly which leads to potential misjudgements. Tradi...
Algorithms Image Processing, Computer-Assisted Plant Leaves Deep Learning Plant Diseases Manihot
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T1-weighted MRI-based brain tumor classification using hybrid deep learning models.

Scientific reports Feb 27, 2025
Health is fundamental to human well-being, with brain health particularly critical for cognitive functions. Magnetic resonance imaging (MRI) serves as a cornerstone in diagnosing brain health issues, providing essential data for healthcare decisions....
Meningioma Brain Neoplasms Deep Learning Glioma Image Processing, Computer-Assisted Magnetic Resonance Imaging Humans Neural Networks, Computer
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Deeply supervised two stage generative adversarial network for stain normalization.

Scientific reports Feb 27, 2025
The color variations present in histopathological images pose a significant challenge to computational pathology and, consequently, negatively affect the performance of certain pathological image analysis methods, especially those based on deep learn...
Deep Learning Image Processing, Computer-Assisted Generative Adversarial Networks Staining and Labeling Neural Networks, Computer Algorithms Humans
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An AI-based tool for prosthetic crown segmentation serving automated intraoral scan-to-CBCT registration in challenging high artifact scenarios.

The Journal of prosthetic dentistry Feb 26, 2025
STATEMENT OF PROBLEM: Accurately registering intraoral and cone beam computed tomography (CBCT) scans in patients with metal artifacts poses a significant challenge. Whether a cloud-based platform trained for artificial intelligence (AI)-driven segme...
Algorithms Humans Cone-Beam Computed Tomography Artifacts Artificial Intelligence Image Processing, Computer-Assisted Crowns
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Data-efficient generalization of AI transformers for noise reduction in ultra-fast lung PET scans.

European journal of nuclear medicine and molecular imaging Feb 26, 2025
PURPOSE: Respiratory motion during PET acquisition may produce lesion blurring. Ultra-fast 20-second breath-hold (U2BH) PET reduces respiratory motion artifacts, but the shortened scanning time increases statistical noise and may affect diagnostic qu...
Deep Learning Artifacts Humans Time Factors Positron Emission Tomography Computed Tomography Signal-To-Noise Ratio Lung Image Processing, Computer-Assisted Positron-Emission Tomography Breath Holding
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Multi-axis transformer based U-Net with class balanced ensemble model for lung disease classification using X-ray images.

Journal of X-ray science and technology Feb 26, 2025
Chest X-rays are an essential diagnostic tool for identifying chest disorders because of its high sensitivity in detecting pathological anomalies in the lungs. Classification models based on conventional Convolutional Neural Networks (CNNs) are adve...
Deep Learning Algorithms Lung Diseases Lung Image Processing, Computer-Assisted Radiography, Thoracic Humans Neural Networks, Computer Radiographic Image Interpretation, Computer-Assisted
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ADMM-TransNet: ADMM-Based Sparse-View CT Reconstruction Method Combining Convolution and Transformer Network.

Tomography (Ann Arbor, Mich.) Feb 26, 2025
BACKGROUND: X-ray computed tomography (CT) imaging technology provides high-precision anatomical visualization of patients and has become a standard modality in clinical diagnostics. A widely adopted strategy to mitigate radiation exposure is sparse-...
Algorithms Tomography, X-Ray Computed Radiographic Image Interpretation, Computer-Assisted Humans Deep Learning Image Processing, Computer-Assisted Neural Networks, Computer
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Enhanced glioma tumor detection and segmentation using modified deep learning with edge fusion and frequency features.

Scientific reports Feb 26, 2025
Computer-aided automatic brain tumor detection is crucial for timely diagnosis and treatment, especially in regions with limited access to medical expertise. However, existing methods often overlook edge pixel information during tumor segmentation, l...
Brain Neoplasms Deep Learning Algorithms Reproducibility of Results Magnetic Resonance Imaging Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Glioma Humans
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CycleH-CUT: an unsupervised medical image translation method based on cycle consistency and hybrid contrastive learning.

Physics in medicine and biology Feb 26, 2025
Unsupervised medical image translation tasks are challenging due to the difficulty of obtaining perfectly paired medical images. CycleGAN-based methods have proven effective in unpaired medical image translation. However, these methods can produce ar...
Humans Image Processing, Computer-Assisted Unsupervised Machine Learning
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Exploring the values underlying machine learning research in medical image analysis.

Medical image analysis Feb 25, 2025
Machine learning has emerged as a crucial tool for medical image analysis, largely due to recent developments in deep artificial neural networks addressing numerous, diverse clinical problems. As with any conceptual tool, the effective use of machine...
Diagnostic Imaging Biomedical Research Image Processing, Computer-Assisted Machine Learning Humans Neural Networks, Computer Algorithms
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