activity
20232025
most citedA comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis

33 citations · 40 across the 8 of their papers we have counts for

collaborators

8 papers

cs.CV2025

Multimodal Carotid Risk Stratification with Large Vision-Language Models: Benchmarking, Fine-Tuning, and Clinical Insights

Daphne Tsolissou, Theofanis Ganitidis, Konstantinos Mitsis +3

Reliable risk assessment for carotid atheromatous disease remains a major clinical challenge, as it requires integrating diverse clinical and imaging information in a manner that i…

cs.LG2025

Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images

Maria Eleftheria Vlontzou, Maria Athanasiou, Christos Davatzikos +1

The present study performs a comprehensive fairness analysis of machine learning (ML) models for the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) from…

cs.LG2025★ 3 cited

A modular framework for automated evaluation of procedural content generation in serious games with deep reinforcement learning agents

Eleftherios Kalafatis, Konstantinos Mitsis, Konstantia Zarkogianni +2

Serious Games (SGs) are nowadays shifting focus to include procedural content generation (PCG) in the development process as a means of offering personalized and enhanced player ex…

cs.LG2024★ 33 cited

A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis

Maria Eleftheria Vlontzou, Maria Athanasiou, Kalliopi Dalakleidi +3

An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of th…

cs.SD2024★ 4 cited

Sustaining model performance for covid-19 detection from dynamic audio data: Development and evaluation of a comprehensive drift-adaptive framework

Theofanis Ganitidis, Maria Athanasiou, Konstantinos Mitsis +2

Background: The COVID-19 pandemic has highlighted the need for robust diagnostic tools capable of detecting the disease from diverse and evolving data sources. Machine learning mod…

cs.HC2024

AIris: An AI-powered Wearable Assistive Device for the Visually Impaired

Dionysia Danai Brilli, Evangelos Georgaras, Stefania Tsilivaki +2

Assistive technologies for the visually impaired have evolved to facilitate interaction with a complex and dynamic world. In this paper, we introduce AIris, an AI-powered wearable…