Focal Loss and Adversarial Autoencoders for Alzheimer's Disease Diagnosis and Biomarker Identification
A novel normative modeling approach using focal loss and adversarial autoencoders (FAAE) improves Alzheimer's Disease diagnosis and biomarker identification by enhancing the detection of complex cases and offering insights into regional brain deviations.
그룹 강건성을 위한 분리 분류기 및 적응형 선형 변조를 활용한 MCI에서 AD로의 변환에 대한 조기 진단 향상: DEAL
본 논문에서는 자기 공명 영상(MRI) 기반 알츠하이머병(AD) 진단에서 그룹 강건성 문제를 다루는 새로운 딥러닝 방법론인 DEAL(Decoupled Classifier with Adaptive Linear Modulation)을 제안합니다.
Improving the Accuracy of Early Alzheimer's Disease Diagnosis Using MRI and Patient Information: The DEAL Method
Deep learning models can be used to predict the conversion from mild cognitive impairment (MCI) to Alzheimer's disease (AD) using MRI images, but these models often underperform for certain age groups. The DEAL method improves accuracy across age groups by combining MRI data with easily obtainable patient information (age, cognitive test scores, and education level) and using a decoupled classifier tailored to different age ranges.
A Deep Learning Framework for Diagnosing Alzheimer's Disease Using Neuroimaging and Genomic Data
Integrating structural and functional MRI data with Single Nucleotide Polymorphism (SNP) information in a deep learning framework that handles missing data can effectively detect Alzheimer's disease and predict MCI conversion, offering valuable biological insights.
International Working Group Challenges Alzheimer's Association's New Diagnostic Criteria for Alzheimer's Disease
The International Working Group (IWG) argues against diagnosing Alzheimer's disease solely on biomarkers in cognitively normal individuals, emphasizing the potential harms of such a diagnosis and proposing alternative terminology focused on risk stratification.
BG-GAN: Using AI to Generate and Analyze Brain Structure-Function Connections in Alzheimer's Disease
This research proposes a novel AI framework, BG-GAN, which leverages graph generative adversarial networks to analyze the complex relationship between brain structure and function in Alzheimer's disease, potentially leading to improved diagnostic accuracy.
Combining Classical and Deep Neuroimaging Biomarkers for Enhanced Early Alzheimer's Disease Diagnosis Using MRI
Integrating multiple imaging biomarkers, particularly radiomics and hippocampal texture descriptors, significantly improves the accuracy of early Alzheimer's disease diagnosis using MRI, outperforming deep learning models in this study.
Flexi-Fuzz Least Squares Support Vector Machine for Improved Alzheimer's Disease Diagnosis: A Novel Approach to Address Noise, Outliers, and Class Imbalance
This paper introduces a novel machine learning model, Flexi-Fuzz-LSSVM, which leverages a robust membership scheme and the median as a class-center determination method to improve the accuracy of Alzheimer's disease diagnosis, particularly in handling noisy and imbalanced datasets.
Early Detection and Diagnosis of Alzheimer's Disease in Primary Care
Early detection of cognitive impairment is crucial, and primary care physicians can play a vital role by utilizing screening tools like the Mini-Cog and MoCA to identify potential Alzheimer's disease cases and initiate appropriate interventions.
Ricci Flow-Based Brain Surface Covariance Descriptors for Alzheimer's Disease Diagnosis
신경퇴행성 질환 진단을 위한 Ricci 플로우 기반 뇌 표면 공분산 기술
© 2024 by Linnk AI