AIMC Topic: Multimodal Imaging
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Multi-modal MRI synthesis with conditional latent diffusion models for data augmentation in tumor segmentation.
Computerized medical imaging and graphics : the official journal of the Computerized Medical Imaging Society
Mar 21, 2025
Multimodality is often necessary for improving object segmentation tasks, especially in the case of multilabel tasks, such as tumor segmentation, which is crucial for clinical diagnosis and treatment planning. However, a major challenge in utilizing ...
Dual-Modality Virtual Biopsy System Integrating MRI and MG for Noninvasive Predicting HER2 Status in Breast Cancer.
Academic radiology
Mar 10, 2025
RATIONALE AND OBJECTIVES: Accurate determination of human epidermal growth factor receptor 2 (HER2) expression is critical for guiding targeted therapy in breast cancer. This study aimed to develop and validate a deep learning (DL)-based decision-mak...
Diagnostic value of deep learning of multimodal imaging of thyroid for TI-RADS category 3-5 classification.
Endocrine
Mar 8, 2025
BACKGROUND: Thyroid nodules classified within the Thyroid Imaging Reporting and Data Systems (TI-RADS) category 3-5 are typically regarded as having varying degrees of malignancy risk, with the risk increasing from TI-RADS 3 to TI-RADS 5. While some ...
Towards automatic US-MR fetal brain image registration with learning-based methods.
NeuroImage
Mar 7, 2025
Fetal brain imaging is essential for prenatal care, with ultrasound (US) and magnetic resonance imaging (MRI) providing complementary strengths. While MRI has superior soft tissue contrast, US offers portable and inexpensive screening of neurological...
FFSWOAFuse: Multi-modal medical image fusion via fermatean fuzzy set and whale optimization algorithm.
Computers in biology and medicine
Mar 6, 2025
Multi-modal medical image fusion (MMIF) plays a crucial role in obtaining valuable and significant information from different medical modalities. This process has generated a single resultant image that is suitable for better clinical assessments and...
CSEPC: a deep learning framework for classifying small-sample multimodal medical image data in Alzheimer's disease.
BMC geriatrics
Feb 26, 2025
BACKGROUND: Alzheimer's disease (AD) is a neurodegenerative disorder that significantly impacts health care worldwide, particularly among the elderly population. The accurate classification of AD stages is essential for slowing disease progression an...
Hyperfusion: A hypernetwork approach to multimodal integration of tabular and medical imaging data for predictive modeling.
Medical image analysis
Feb 21, 2025
The integration of diverse clinical modalities such as medical imaging and the tabular data extracted from patients' Electronic Health Records (EHRs) is a crucial aspect of modern healthcare. Integrative analysis of multiple sources can provide a com...
Prediction of adverse pathology in prostate cancer using a multimodal deep learning approach based on [F]PSMA-1007 PET/CT and multiparametric MRI.
European journal of nuclear medicine and molecular imaging
Feb 19, 2025
PURPOSE: Accurate prediction of adverse pathology (AP) in prostate cancer (PCa) patients is crucial for formulating effective treatment strategies. This study aims to develop and evaluate a multimodal deep learning model based on [F]PSMA-1007 PET/CT ...
Diffusion-driven multi-modality medical image fusion.
Medical & biological engineering & computing
Feb 11, 2025
Multi-modality medical image fusion (MMIF) technology utilizes the complementarity of different modalities to provide more comprehensive diagnostic insights for clinical practice. Existing deep learning-based methods often focus on extracting the pri...
Multi-modality medical image classification with ResoMergeNet for cataract, lung cancer, and breast cancer diagnosis.
Computers in biology and medicine
Feb 11, 2025
The variability in image modalities presents significant challenges in medical image classification, as traditional deep learning models often struggle to adapt to different image types, leading to suboptimal performance across diverse datasets. This...
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