4 papers
From Image Latent Space to Fuzzy Rules: Interpretable Analysis of Gastrointestinal Foundation Model
Michael D. Vasilakakis, Dimitris K. Iakovidis
Foundation models pretrained on large-scale datasets demonstrate strong transferability to medical imaging tasks. However, understanding how their latent representations encode cli…
Interpretable Synthetic Medical Tabular Data Generation for Clinical Decision Support Using Fuzzy Cognitive Maps
Michael Vasilakakis, Dimitris K. Iakovidis
Synthetic medical tabular data generation has become essential for developing and validating computer-based medical systems (CBMSs) when real clinical data is restricted due to pri…
Fuzzy Distribution Modeling for Synthetic Tabular Data Generation with Causality Preservation
Michael Vasilakakis, Dimitris K. Iakovidis
Synthetic tabular data generation provides an effective alternative for the training of machine learning models when real-world data is limited or inaccessible. However, the hetero…
Intuitionistic Fuzzy Cognitive Maps for Interpretable Image Classification
Georgia Sovatzidi, Michael D. Vasilakakis, Dimitris K. Iakovidis
Several deep learning (DL) approaches have been proposed to deal with image classification tasks. However, despite their effectiveness, they lack interpretability, as they are unab…