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...
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 ...
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...
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...
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...
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...
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 ...
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...
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...
European journal of nuclear medicine and molecular imaging
Feb 6, 2025
PURPOSE: To address the challenges of verifying MR-based attenuation correction (MRAC) in PET/MR due to CT positional mismatches and alignment issues, this study utilized a flatbed insert and arms-down positioning during PET/CT scans to achieve preci...
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