:: Library Catalog

Copertina

Salvato in:

Dettagli Bibliografici
Autori principali:	Vlontzou, Maria Eleftheria, Athanasiou, Maria, Dalakleidi, Kalliopi, Skampardoni, Ioanna, Davatzikos, Christos, Nikita, Konstantina
Natura:	Preprint
Pubblicazione:	2024
Soggetti:	Machine Learning
Accesso online:	https://arxiv.org/abs/2412.09376
Tags:	Aggiungi Tag Nessun Tag, puoi essere il primo ad aggiungerne!!

Documenti analoghi

Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images
di: Vlontzou, Maria Eleftheria, et al.
Pubblicazione: (2025)

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents
di: Kalafatis, Eleftherios, et al.
Pubblicazione: (2025)

Dimensional Neuroimaging Endophenotypes: Neurobiological Representations of Disease Heterogeneity Through Machine Learning
di: Wen, Junhao, et al.
Pubblicazione: (2024)

Dominant Trajectories of Aging‐related Brain Atrophy Identified in 48,949 Individuals via a coupled Cross‐sectional and Longitudinal Non‐negative Matrix Factorization
di: Ioanna Skampardoni, et al.
Pubblicazione: (2025)

Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework
di: Ganitidis, Theofanis, et al.
Pubblicazione: (2024)

DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
di: Nguyen-Quang, Thinh, et al.
Pubblicazione: (2026)

Federated Learning for Predicting Mild Cognitive Impairment to Dementia Conversion
di: Sharma, Gaurang, et al.
Pubblicazione: (2025)

A unified framework for evaluating the robustness of machine-learning interpretability for prospect risking
di: Chowdhury, Prithwijit, et al.
Pubblicazione: (2026)

Adaptive Shrinkage Estimation For Personalized Deep Kernel Regression In Modeling Brain Trajectories
di: Tassopoulou, Vasiliki, et al.
Pubblicazione: (2025)

Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cognitive Impairment
di: Mostafa, Fahad, et al.
Pubblicazione: (2025)

Predicting Mild Cognitive Impairment Using Naturalistic Driving and Trip Destination Modeling
di: Chattopadhyay, Souradeep, et al.
Pubblicazione: (2025)

Linguistic-Based Mild Cognitive Impairment Detection Using Informative Loss
di: Fard, Ali Pourramezan, et al.
Pubblicazione: (2024)

Linguistic and Audio Embedding-Based Machine Learning for Alzheimer's Dementia and Mild Cognitive Impairment Detection: Insights from the PROCESS Challenge
di: Devahi, Adharsha Sam Edwin Sam, et al.
Pubblicazione: (2025)

Are machine learning interpretations reliable? A stability study on global interpretations
di: Gan, Luqin, et al.
Pubblicazione: (2025)

Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls
di: Khazaee, Ali, et al.
Pubblicazione: (2024)

Judgment in Mild Cognitive Impairment and Alzheimer’s disease
di: Patrícia Helena Figueirêdo Vale Capucho
Pubblicazione: (2011)

Electrocardiogram-based diagnosis of liver diseases: an externally validated and explainable machine learning approach
di: Alcaraz, Juan Miguel Lopez, et al.
Pubblicazione: (2024)

A machine learning framework for interpretable predictions in patient pathways: The case of predicting ICU admission for patients with symptoms of sepsis
di: Zilker, Sandra, et al.
Pubblicazione: (2024)

Adapting Machine Learning Diagnostic Models to New Populations Using a Small Amount of Data: Results from Clinical Neuroscience
di: Wang, Rongguang, et al.
Pubblicazione: (2023)

A Machine Learning Approach for Identifying Anatomical Biomarkers of Early Mild Cognitive Impairment
di: Ahmad, Alwani Liyana, et al.
Pubblicazione: (2024)

META-ANOVA: Screening interactions for interpretable machine learning
di: Choi, Yongchan, et al.
Pubblicazione: (2024)

Unveil Multi-Picture Descriptions for Multilingual Mild Cognitive Impairment Detection via Contrastive Learning
di: Qi, Kristin, et al.
Pubblicazione: (2025)

mlr3summary: Concise and interpretable summaries for machine learning models
di: Dandl, Susanne, et al.
Pubblicazione: (2024)

An efficient, accurate, and interpretable machine learning method for computing probability of failure
di: Zhu, Jacob, et al.
Pubblicazione: (2026)

Analyzing Multimodal Features of Spontaneous Voice Assistant Commands for Mild Cognitive Impairment Detection
di: Lin, Nana, et al.
Pubblicazione: (2024)

Uncertainty-Calibrated Prediction of Randomly-Timed Biomarker Trajectories with Conformal Bands
di: Tassopoulou, Vasiliki, et al.
Pubblicazione: (2025)

Prospective memory in Alzheimer’s disease and Mild Cognitive Impairment
di: Lívia Spíndola
Pubblicazione: (2011)

Assessment for apraxia in Mild Cognitive Impairment and Alzheimer’s disease
di: Mirela Ward
Pubblicazione: (2015)

Genetically‐informed parcellation reveals interpretable brain patterns and enhanced genetic associations in Alzheimer's Disease compared to structural‐only approaches
di: Marilena De Pian, et al.
Pubblicazione: (2025)

CogniVoice: Multimodal and Multilingual Fusion Networks for Mild Cognitive Impairment Assessment from Spontaneous Speech
di: Cheng, Jiali, et al.
Pubblicazione: (2024)

Recent advances in interpretable machine learning using structure-based protein representations
di: Vecchietti, Luiz Felipe, et al.
Pubblicazione: (2024)

Machine learning-enhanced non-amnestic Alzheimer's disease diagnosis from MRI and clinical features
di: Witherow, Megan A., et al.
Pubblicazione: (2026)

XAI4Extremes: An interpretable machine learning framework for understanding extreme-weather precursors under climate change
di: Wei, Jiawen, et al.
Pubblicazione: (2025)

LLM-based feature generation from text for interpretable machine learning
di: Balek, Vojtěch, et al.
Pubblicazione: (2024)

Early diagnosis of Alzheimer's disease from MRI images with deep learning model
di: Javid, Sajjad Aghasi, et al.
Pubblicazione: (2024)

GraphStorm: all-in-one graph machine learning framework for industry applications
di: Zheng, Da, et al.
Pubblicazione: (2024)

On the definition and importance of interpretability in scientific machine learning
di: Rowan, Conor, et al.
Pubblicazione: (2025)

Leveraging advances in machine learning for the robust classification and interpretation of networks
di: Appaw, Raima Carol, et al.
Pubblicazione: (2024)

Heart Rate and its Variability from Short-term ECG Recordings as Biomarkers for Detecting Mild Cognitive Impairment in Indian Population
di: Xavier, Anjo, et al.
Pubblicazione: (2024)

Explainable and externally validated machine learning for neurocognitive diagnosis via electrocardiograms
di: Alcaraz, Juan Miguel Lopez, et al.
Pubblicazione: (2025)