EPDTNet + -EM: Advanced Transfer Learning and Subnet Architecture for Medical Image Diagnosis

Information Science, Artificial Intelligence, Medicine, Medical Imaging,
medical image classification   enhanced exponential transfer learning   parallel subnet (PSNet +)   explainable AI (XAI)   feature extraction   abnormality detection   computational efficiency  

Academic Background In today’s healthcare environment, medical imaging plays a crucial role in disease diagnosis, treatment planning, and health management. However, traditional medical image analysis methods face numerous challenges, such as overfitting, high computational costs, limited generalization capabilities, and issues related to noise, si...

Investigating Brain Lobe Biomarkers to Enhance Dementia Detection Using EEG Data

Information Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging,
Electroencephalogram   Dementia   Alzheimer's Disease   Frontotemporal Dementia   Short-Time Fourier Transform   Convolutional Neural Networks  

Background Introduction Dementia is a global health issue that significantly impacts patients’ quality of life and places a substantial burden on healthcare systems. Alzheimer’s Disease (AD) and Frontotemporal Dementia (FTD) are two common types of dementia, and their overlapping symptoms make accurate diagnosis and targeted treatment development p...

MediVision: Empowering Colorectal Cancer Diagnosis and Tumor Localization through Supervised Learning Classifications and Grad-CAM Visualization of Medical Colonoscopy Images

Information Science, Artificial Intelligence, Medicine, Medical Imaging, Oncology,
Colorectal Cancer   Colonoscopy   Convolutional Neural Networks   Malignant Tumors   Integrated CNN   Grad-CAM  

Academic Background Colorectal Cancer (CRC) is one of the most common cancers worldwide, particularly among individuals over the age of 50. Early detection and accurate diagnosis are crucial for improving patient survival rates. However, traditional CRC screening methods, such as colonoscopy, rely heavily on the experience and visual judgment of ph...

Efficacy of 3D-TSE Sequence-Based Radiosurgery in Prolonging Time to Distant Intracranial Failure: A Session-Wise Analysis in a Histology-Diverse Patient Cohort

Medicine, Neurology, Medical Imaging, Oncology,
Radiosurgery   3D-TSE sequence   Distant Intracranial Failure   Brain Metastases   Imaging Protocol  

Efficacy of 3D-TSE Sequence in Prolonging Time to Distant Intracranial Failure: A Session-Wise Analysis in a Histology-Diverse Patient Cohort Academic Background Brain metastases (BM) represent the majority of intracranial malignancies and significantly contribute to cancer-related morbidity and mortality. At the initial diagnosis of systemic cance...

Prospective Longitudinal Analysis of Physiologic MRI-Based Tumor Habitat Predicts Short-Term Patient Outcomes in IDH-Wildtype Glioblastoma

Medicine, Neurology, Neurosurgery, Medical Imaging, Oncology,
glioblastoma   tumor habitat   physiologic MRI   prognosis prediction   IDH-wildtype  

Prospective Longitudinal Analysis of Physiologic MRI-Based Tumor Habitat Predicts Short-Term Patient Outcomes in IDH-Wildtype Glioblastoma Academic Background Glioblastoma (GBM) is a highly malignant brain tumor characterized by significant intratumoral heterogeneity, which is evident not only in gene expression and histopathology but also in macro...

ABVS Breast Tumour Segmentation via Integrating CNN with Dilated Sampling Self-Attention and Feature Interaction Transformer

Information Science, Artificial Intelligence, Medicine, Medical Imaging,
Breast Tumour Segmentation   Automated Breast Volume Scanner   3D Transformer-CNN Segmentation Network   Mask Image Modelling   Deep Learning   Medical Image Analysis   Self-Supervised Learning  

ABVS Breast Tumor Segmentation Research Based on CNN and Dilated Sampling Self-Attention Academic Background Breast cancer is the second most common cancer worldwide, and early and accurate detection is crucial for improving patient prognosis and reducing mortality. Although various imaging techniques (such as X-ray mammography, magnetic resonance ...

Comprehensive Evaluation of Pipelines for Classification of Psychiatric Disorders Using Multi-Site Resting-State fMRI Datasets

Comprehensive Evaluation of Pipelines for Classification of Psychiatric Disorders Using Multi-Site Resting-State fMRI Datasets

Medicine, Psychiatry, Neurology, Medical Imaging,
classification biomarker   pipeline   generalization   fMRI   multi-site dataset   major depressive disorder  

Background Introduction The field of psychiatry has long relied on symptoms and medical interviews for diagnosis, lacking objective biomarkers. Resting-state functional magnetic resonance imaging (rs-fMRI) is widely believed to reveal characteristic patterns of brain structure and function, thereby providing potential classification markers for the...

Anxiety Disorder Identification with Biomarker Detection through Subspace-Enhanced Hypergraph Neural Network

Anxiety Disorder Identification with Biomarker Detection through Subspace-Enhanced Hypergraph Neural Network

Information Science, Artificial Intelligence, Medicine, Psychiatry, Medical Imaging, Life Sciences, Bioengineering,
anxiety disorder   hypergraph neural network   multimodal data   subspace enhancement   biomarker  

Anxiety Disorder Identification and Biomarker Detection Based on Subspace-Enhanced Hypergraph Neural Network Academic Background Anxiety Disorders (ADs) are prevalent mental health issues globally, affecting approximately 7.3% of the population. Patients with anxiety disorders typically exhibit excessive fear, worry, and related behavioral abnormal...

Temporal Autocorrelation is Predictive of Age—An Extensive MEG Time-Series Analysis

Medicine, Geriatrics, Neurology, Medical Imaging, Life Sciences, Bioengineering, Biophysics,
temporal autocorrelation   brain age   resting-state   electrophysiology   lifespan   autocorrelation  

Brain Age Prediction Study Based on MEG Time Series Academic Background With the extension of human lifespan, understanding changes in the brain throughout the life cycle has become increasingly important. The structure and function of the brain undergo significant changes with age, which not only affect cognitive functions but are also closely rel...

Deep Learning to Quantify the Pace of Brain Aging in Relation to Neurocognitive Changes

Information Science, Computer Science, Artificial Intelligence, Medicine, Neurology, Medical Imaging, Life Sciences, Bioengineering,
deep learning   brain aging   neurocognitive changes   Alzheimer's disease   magnetic resonance imaging  

As the global aging problem intensifies, the incidence of neurodegenerative diseases (such as Alzheimer’s Disease, AD) is increasing year by year. Brain aging (Brain Aging, BA) is one of the significant risk factors for neurodegenerative diseases, but it does not completely align with chronological age (Chronological Age, CA). Traditional methods f...