5 papers
A Concept-based approach to Voice Disorder Detection
Davide Ghia, Gabriele Ciravegna, Alkis Koudounas +4
Voice disorders affect a significant portion of the population, and the ability to diagnose them using automated, non-invasive techniques would represent a substantial advancement…
Towards Better Generalization and Interpretability in Unsupervised Concept-Based Models
Francesco De Santis, Philippe Bich, Gabriele Ciravegna +3
To increase the trustworthiness of deep neural networks, it is critical to improve the understanding of how they make decisions. This paper introduces a novel unsupervised concept-…
MVP: Multi-source Voice Pathology detection
Alkis Koudounas, Moreno La Quatra, Gabriele Ciravegna +6
Voice disorders significantly impact patient quality of life, yet non-invasive automated diagnosis remains under-explored due to both the scarcity of pathological voice data, and t…
V-CEM: Bridging Performance and Intervenability in Concept-based Models
Francesco De Santis, Gabriele Ciravegna, Philippe Bich +2
Concept-based eXplainable AI (C-XAI) is a rapidly growing research field that enhances AI model interpretability by leveraging intermediate, human-understandable concepts. This app…
Prime Convolutional Model: Breaking the Ground for Theoretical Explainability
Francesco Panelli, Doaa Almhaithawi, Tania Cerquitelli +1
In this paper, we propose a new theoretical approach to Explainable AI. Following the Scientific Method, this approach consists in formulating on the basis of empirical evidence, a…