AIMC Topic: Image Processing, Computer-Assisted

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PEARL: Cascaded Self-Supervised Cross-Fusion Learning for Parallel MRI Acceleration.

IEEE journal of biomedical and health informatics May 6, 2025
Supervised deep learning (SDL) methodology holds promise for accelerated magnetic resonance imaging (AMRI) but is hampered by the reliance on extensive training data. Some self-supervised frameworks, such as deep image prior (DIP), have emerged, elim...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Supervised Machine Learning Humans Algorithms Brain Deep Learning
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MDEU-Net: Medical Image Segmentation Network Based on Multi-Head Multi-Scale Cross-Axis.

Sensors (Basel, Switzerland) May 5, 2025
Significant advances have been made in the application of attention mechanisms to medical image segmentation, and these advances are notably driven by the development of the cross-axis attention mechanism. However, challenges remain in handling compl...
Diagnostic Imaging Algorithms Image Processing, Computer-Assisted Humans Neural Networks, Computer
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Latent space autoencoder generative adversarial model for retinal image synthesis and vessel segmentation.

BMC medical imaging May 5, 2025
Diabetes is a widespread condition that can lead to serious vision problems over time. Timely identification and treatment of diabetic retinopathy (DR) depend on accurately segmenting retinal vessels, which can be achieved through the invasive techni...
Retinal Vessels Deep Learning Neural Networks, Computer Image Interpretation, Computer-Assisted Fundus Oculi Humans Algorithms Image Processing, Computer-Assisted Autoencoder Diabetic Retinopathy
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Integrating SAM priors with U-Net for enhanced multiclass cell detection in digital pathology.

Scientific reports May 5, 2025
In digital pathology, the accurate detection, segmentation, and classification of cells are pivotal for precise pathological diagnosis. Traditionally, pathologists manually segment cells from pathological images to facilitate diagnosis based on these...
Neural Networks, Computer Algorithms Image Processing, Computer-Assisted Humans Image Interpretation, Computer-Assisted
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Advanced holographic convolutional dense networks and Tangent runner optimization for enhanced polycystic ovarian disease classification.

Scientific reports May 5, 2025
Polycystic Ovarian Disease (PCOD) is among the most prevalent endocrine disorders complicating the health of innumerable women worldwide due to lack of diagnosis and appropriate management. The diagnosis of PCOD, along with proper classification with...
Polycystic Ovary Syndrome Holography Algorithms Ultrasonography Female Humans Image Processing, Computer-Assisted Deep Learning
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GelGenie: an AI-powered framework for gel electrophoresis image analysis.

Nature communications May 5, 2025
Gel electrophoresis is a ubiquitous laboratory method for the separation and semi-quantitative analysis of biomolecules. However, gel image analysis principles have barely advanced for decades, in stark contrast to other fields where AI has revolutio...
Humans Artificial Intelligence Algorithms Image Processing, Computer-Assisted Software Electrophoresis, Polyacrylamide Gel
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Accelerated ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging of macromolecular fraction (MMF) in cortical bone based on a self-attention convolutional neural network.

Magnetic resonance imaging May 4, 2025
PURPOSE: To combine ultrashort echo time quantitative magnetization transfer (UTE-qMT) imaging with a self-attention convolutional neural network (SAT-Net) for accelerated mapping of macromolecular fraction (MMF) in cortical bone.
Convolutional Neural Networks Image Processing, Computer-Assisted Magnetic Resonance Imaging Adult Middle Aged Cortical Bone Tibia Bone Diseases, Metabolic Neural Networks, Computer Algorithms Female Young Adult Osteoporosis Humans
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Self-supervised learning for MRI reconstruction through mapping resampled k-space data to resampled k-space data.

Magnetic resonance imaging May 3, 2025
In recent years, significant advancements have been achieved in applying deep learning (DL) to magnetic resonance imaging (MRI) reconstruction, which traditionally relies on fully sampled data. However, real-world clinical scenarios often demonstrate...
Humans Algorithms Image Processing, Computer-Assisted Brain Deep Learning Magnetic Resonance Imaging Supervised Machine Learning
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Delving into transfer learning within U-Net for refined retinal vessel segmentation: An extensive hyperparameter analysis.

Photodiagnosis and photodynamic therapy May 3, 2025
Blood vessel segmentation poses numerous challenges. Firstly, blood vessels often lack sufficient contrast against the background, impeding accurate differentiation. Additionally, the overlapping nature of blood vessels complicates separating individ...
Image Processing, Computer-Assisted Humans Retinal Vessels Neural Networks, Computer Algorithms Deep Learning Machine Learning
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Optimizing stroke lesion segmentation: A dual-approach using Gaussian mixture models and nnU-Net.

Computers in biology and medicine May 2, 2025
Machine learning-based stroke lesion segmentation models are widely used in biomedical imaging, but their ability to detect treatment effects remains largely unexplored. Gaussian Mixture Models (GMM) and nnU-Net are among the most prominent and well-...
Humans Machine Learning Deep Learning Stroke Image Processing, Computer-Assisted Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Normal Distribution
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