AIMC Topic: Multimodal Imaging

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Attention-based Fusion Network for Breast Cancer Segmentation and Classification Using Multi-modal Ultrasound Images.

Ultrasound in medicine & biology Dec 17, 2024
OBJECTIVE: Breast cancer is one of the most commonly occurring cancers in women. Thus, early detection and treatment of cancer lead to a better outcome for the patient. Ultrasound (US) imaging plays a crucial role in the early detection of breast can...
Humans Female Ultrasonography, Mammary Breast Neoplasms Breast Deep Learning Image Interpretation, Computer-Assisted Multimodal Imaging
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Predicting axillary lymph node metastasis in breast cancer using a multimodal radiomics and deep learning model.

Frontiers in immunology Dec 13, 2024
OBJECTIVE: To explore the value of combined radiomics and deep learning models using different machine learning algorithms based on mammography (MG) and magnetic resonance imaging (MRI) for predicting axillary lymph node metastasis (ALNM) in breast c...
Mammography Multimodal Imaging Radiomics Aged Middle Aged Humans Magnetic Resonance Imaging Lymph Nodes Adult Deep Learning Retrospective Studies Axilla Lymphatic Metastasis Breast Neoplasms Female
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Personalized predictions of Glioblastoma infiltration: Mathematical models, Physics-Informed Neural Networks and multimodal scans.

Medical image analysis Dec 12, 2024
Predicting the infiltration of Glioblastoma (GBM) from medical MRI scans is crucial for understanding tumor growth dynamics and designing personalized radiotherapy treatment plans. Mathematical models of GBM growth can complement the data in the pred...
Precision Medicine Brain Neoplasms Imaging, Three-Dimensional Humans Neural Networks, Computer Algorithms Glioblastoma Image Interpretation, Computer-Assisted Magnetic Resonance Imaging Neoplasm Invasiveness Multimodal Imaging
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CNN-Based Cross-Modality Fusion for Enhanced Breast Cancer Detection Using Mammography and Ultrasound.

Tomography (Ann Arbor, Mich.) Dec 12, 2024
Breast cancer is a leading cause of mortality among women in Taiwan and globally. Non-invasive imaging methods, such as mammography and ultrasound, are critical for early detection, yet standalone modalities have limitations in regard to their diagn...
Female Breast Neoplasms Early Detection of Cancer Ultrasonography, Mammary Breast Mammography Multimodal Imaging Humans Neural Networks, Computer Machine Learning
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Let it shine: Autofluorescence of Papanicolaou-stain improves AI-based cytological oral cancer detection.

Computers in biology and medicine Dec 10, 2024
BACKGROUND AND OBJECTIVES: Oral cancer is a global health challenge. The disease can be successfully treated if detected early, but the survival rate drops significantly for late stage cases. There is a growing interest in a shift from the current st...
Datasets as Topic Optical Imaging Mouth Mucosa Mouth Neoplasms Microscopy Image Interpretation, Computer-Assisted Humans Adult Deep Learning Multimodal Imaging Biopsy Papanicolaou Test Early Detection of Cancer
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Multi-Sensor Learning Enables Information Transfer Across Different Sensory Data and Augments Multi-Modality Imaging.

IEEE transactions on pattern analysis and machine intelligence Dec 4, 2024
Multi-modality imaging is widely used in clinical practice and biomedical research to gain a comprehensive understanding of an imaging subject. Currently, multi-modality imaging is accomplished by post hoc fusion of independently reconstructed images...
Humans Tomography, X-Ray Computed Image Processing, Computer-Assisted Machine Learning Magnetic Resonance Imaging Algorithms Multimodal Imaging Brain
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Segmentation of Low-Grade Brain Tumors Using Mutual Attention Multimodal MRI.

Sensors (Basel, Switzerland) Nov 27, 2024
Early detection and precise characterization of brain tumors play a crucial role in improving patient outcomes and extending survival rates. Among neuroimaging modalities, magnetic resonance imaging (MRI) is the gold standard for brain tumor diagnost...
Algorithms Image Interpretation, Computer-Assisted Brain Neoplasms Humans Astrocytoma Deep Learning Image Processing, Computer-Assisted Magnetic Resonance Imaging Multimodal Imaging
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Multi-scale multimodal deep learning framework for Alzheimer's disease diagnosis.

Computers in biology and medicine Nov 22, 2024
Multimodal neuroimaging data, including magnetic resonance imaging (MRI) and positron emission tomography (PET), provides complementary information about the brain that can aid in Alzheimer's disease (AD) diagnosis. However, most existing deep learni...
Positron-Emission Tomography Image Interpretation, Computer-Assisted Neuroimaging Humans Magnetic Resonance Imaging Neural Networks, Computer Brain Multimodal Imaging Aged Alzheimer Disease Deep Learning
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Predicting the cognitive impairment with multimodal ophthalmic imaging and artificial neural network for community screening.

The British journal of ophthalmology Nov 22, 2024
BACKGROUND/AIMS: To investigate the comprehensive prediction ability for cognitive impairment in a general elder population using the combination of the multimodal ophthalmic imaging and artificial neural networks.
Multimodal Imaging Neural Networks, Computer Predictive Value of Tests Oximetry Female Aged Cognitive Dysfunction Humans Male Mass Screening Middle Aged
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GraFMRI: A graph-based fusion framework for robust multi-modal MRI reconstruction.

Magnetic resonance imaging Nov 17, 2024
PURPOSE: This study introduces GraFMRI, a novel framework designed to address the challenges of reconstructing high-quality MRI images from undersampled k-space data. Traditional methods often suffer from noise amplification and loss of structural de...
Algorithms Brain Humans Neural Networks, Computer Artifacts Image Processing, Computer-Assisted Signal-To-Noise Ratio Magnetic Resonance Imaging Multimodal Imaging
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