AIMC Topic: Multimodal Imaging

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Hierarchical in-out fusion for incomplete multimodal brain tumor segmentation.

Scientific reports Jul 2, 2025
Fusing multimodal data play a crucial role in accurate brain tumor segmentation network and clinical diagnosis, especially in scenarios with incomplete multimodal data. Existing multimodal fusion models usually perform intra-modal fusion at both shal...
Humans Image Processing, Computer-Assisted Magnetic Resonance Imaging Multimodal Imaging Algorithms Brain Neoplasms Neural Networks, Computer
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Enhancing efficient deep learning models with multimodal, multi-teacher insights for medical image segmentation.

Scientific reports May 7, 2025
The rapid evolution of deep learning has dramatically enhanced the field of medical image segmentation, leading to the development of models with unprecedented accuracy in analyzing complex medical images. Deep learning-based segmentation holds signi...
Image Processing, Computer-Assisted Magnetic Resonance Imaging Multimodal Imaging Humans Tomography, X-Ray Computed Deep Learning
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Progressive Distillation With Optimal Transport for Federated Incomplete Multi-Modal Learning of Brain Tumor Segmentation.

IEEE journal of biomedical and health informatics May 6, 2025
Multi-modal Magnetic Resonance Imaging (MRI) provide sufficient complementary information for brain tumor segmentation, however, most current approaches rely on complete modalities and may collapse with incomplete modalities. Moreover, most existing ...
Image Interpretation, Computer-Assisted Humans Machine Learning Multimodal Imaging Brain Brain Neoplasms Magnetic Resonance Imaging Algorithms Databases, Factual
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MACTFusion: Lightweight Cross Transformer for Adaptive Multimodal Medical Image Fusion.

IEEE journal of biomedical and health informatics May 6, 2025
Multimodal medical image fusion aims to integrate complementary information from different modalities of medical images. Deep learning methods, especially recent vision Transformers, have effectively improved image fusion performance. However, there ...
Image Processing, Computer-Assisted Multimodal Imaging Humans Brain Deep Learning Algorithms
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Adaptive Cross-Feature Fusion Network With Inconsistency Guidance for Multi-Modal Brain Tumor Segmentation.

IEEE journal of biomedical and health informatics May 6, 2025
In the context of contemporary artificial intelligence, increasing deep learning (DL) based segmentation methods have been recently proposed for brain tumor segmentation (BraTS) via analysis of multi-modal MRI. However, known DL-based works usually d...
Image Interpretation, Computer-Assisted Brain Brain Neoplasms Magnetic Resonance Imaging Multimodal Imaging Deep Learning Humans Neural Networks, Computer Algorithms
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A deep learning-based multimodal medical imaging model for breast cancer screening.

Scientific reports Apr 26, 2025
In existing breast cancer prediction research, most models rely solely on a single type of imaging data, which limits their performance. To overcome this limitation, the present study explores breast cancer prediction models based on multimodal medic...
Area Under Curve Breast Neoplasms Middle Aged Mammography Deep Learning Multimodal Imaging ROC Curve Aged Adult Humans Early Detection of Cancer Sensitivity and Specificity Female
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A review of multimodal fusion-based deep learning for Alzheimer's disease.

Neuroscience Apr 24, 2025
Alzheimer's Disease (AD) as one of the most prevalent neurodegenerative disorders worldwide, characterized by significant memory and cognitive decline in its later stages, severely impacting daily lives. Consequently, early diagnosis and accurate ass...
Brain Deep Learning Magnetic Resonance Imaging Humans Neuroimaging Alzheimer Disease Positron-Emission Tomography Multimodal Imaging
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SynMSE: A multimodal similarity evaluator for complex distribution discrepancy in unsupervised deformable multimodal medical image registration.

Medical image analysis Apr 22, 2025
Unsupervised deformable multimodal medical image registration often confronts complex scenarios, which include intermodality domain gaps, multi-organ anatomical heterogeneity, and physiological motion variability. These factors introduce substantial ...
Humans Algorithms Multimodal Imaging Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Brain Tomography, X-Ray Computed Magnetic Resonance Imaging Unsupervised Machine Learning
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A semi-supervised prototypical network for prostate lesion segmentation from multimodality MRI.

Physics in medicine and biology Apr 22, 2025
Prostate lesion segmentation from multiparametric magnetic resonance images is particularly challenging due to the limited availability of labeled data. This scarcity of annotated images makes it difficult for supervised models to learn the complex f...
Prostate Supervised Machine Learning Humans Magnetic Resonance Imaging Algorithms Multimodal Imaging Male Prostatic Neoplasms Image Processing, Computer-Assisted
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Causal recurrent intervention for cross-modal cardiac image segmentation.

Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society Apr 21, 2025
Cross-modal cardiac image segmentation is essential for cardiac disease analysis. In diagnosis, it enables clinicians to obtain more precise information about cardiac structure or function for potential signs by leveraging specific imaging modalities...
Heart Image Interpretation, Computer-Assisted Image Processing, Computer-Assisted Multimodal Imaging Algorithms Deep Learning Humans
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