AIMC Topic: Magnetic Resonance Imaging
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A comparison of different machine learning classifiers in predicting xerostomia and sticky saliva due to head and neck radiotherapy using a multi-objective, multimodal radiomics model.
Biomedical physics & engineering express
Feb 6, 2025
. Although radiotherapy techniques are a primary treatment for head and neck cancer (HNC), they are still associated with substantial toxicity and side effects. Machine learning (ML) based radiomics models for predicting toxicity mostly rely on featu...
Knowledge Distillation Guided Interpretable Brain Subgraph Neural Networks for Brain Disorder Exploration.
IEEE transactions on neural networks and learning systems
Feb 6, 2025
The human brain is a highly complex neurological system that has been the subject of continuous exploration by scientists. With the help of modern neuroimaging techniques, there has been significant progress made in brain disorder analysis. There is ...
Magnetic Resonance-Based Artificial Intelligence- Supported Osteochondral Allograft Transplantation for Massive Osteochondral Defects of the Knee.
Sports medicine and arthroscopy review
Feb 5, 2025
Transplantation of fresh osteochondral allografts is a possible biological resurfacing option to substitute massive bone loss and provide proper gliding surfaces for extended and deep osteochondral lesions of weight-bearing articular surfaces. Limite...
Developing a three-dimensional convolutional neural network for automated full-volume multi-tissue segmentation of the shoulder with comparisons to Goutallier classification and partial volume muscle quality analysis.
Journal of shoulder and elbow surgery
Feb 5, 2025
BACKGROUND: Preoperative intramuscular fat (IMF) is a strong predictor of tendon failure after a rotator cuff repair. Due to the contemporary labor intensive and time-dependent manual segmentation required for quantitative assessment of IMF, clinical...
Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis.
European radiology
Feb 5, 2025
OBJECTIVES: The aim of this work is to evaluate the performance of deep learning (DL) models for breast cancer diagnosis with MRI.
Chan-Vese aided fuzzy C-means approach for whole breast and fibroglandular tissue segmentation: Preliminary application to real-world breast MRI.
Medical physics
Feb 5, 2025
BACKGROUND: Magnetic resonance imaging (MRI) is a highly sensitive modality for diagnosing breast cancer, providing an expanding range of clinical usages that are crucial for the care of women at elevated risk of breast cancer development. Segmentati...
Association between deep learning radiomics based on placental MRI and preeclampsia with fetal growth restriction: A multicenter study.
European journal of radiology
Feb 5, 2025
PURPOSE: Preeclampsia (PE) is associated with placental insufficiency and could lead to adverse pregnancy outcomes. The study aimed to develop a placental T2-weighted image-based automatic quantitative model for the identification of PE pregnancies a...
Detection of Alzheimer Disease in Neuroimages Using Vision Transformers: Systematic Review and Meta-Analysis.
Journal of medical Internet research
Feb 5, 2025
BACKGROUND: Alzheimer disease (AD) is a progressive condition characterized by cognitive decline and memory loss. Vision transformers (ViTs) are emerging as promising deep learning models in medical imaging, with potential applications in the detecti...
Optimizing transformer-based network via advanced decoder design for medical image segmentation.
Biomedical physics & engineering express
Feb 5, 2025
U-Net is widely used in medical image segmentation due to its simple and flexible architecture design. To address the challenges of scale and complexity in medical tasks, several variants of U-Net have been proposed. In particular, methods based on V...
Quantification of tissue stiffness with magnetic resonance elastography and finite difference time domain (FDTD) simulation-based spatiotemporal neural network.
Magnetic resonance imaging
Feb 4, 2025
Quantification of tissue stiffness with magnetic resonance elastography (MRE) is an inverse problem that is sensitive to noise. Conventional methods for the purpose include direct inversion (DI) and local frequency estimation (LFE). In this study, we...
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