| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |
|---|---|---|---|---|---|---|
| 3 | Davydko O., Pavlov V., Biecek P., & Longo L. | SRFAMap: A Method for Mapping Integrated Gradients of a CNN Trained with Statistical Radiomic Features to Medical Image Saliency Maps | eXplainable Artificial Intelligence, The World Conference (xAI-2024) | 2024 |   
@InProceedings{10.1007/978-3-031-63803-9_1, author="Davydko, Oleksandr and Pavlov, Vladimir and Biecek, Przemys{\l}aw and Longo, Luca", editor="Longo, Luca and Lapuschkin, Sebastian and Seifert, Christin", title="SRFAMap: A Method for Mapping Integrated Gradients of a CNN Trained with Statistical Radiomic Features to Medical Image Saliency Maps", booktitle="Explainable Artificial Intelligence", year="2024", publisher="Springer Nature Switzerland", address="Cham", pages="3--23", isbn="978-3-031-63803-9" } [Close]
| Explainable artificial intelligence •
Radiomics •
Texture analysis •
Medical image processing •
Saliency map •
Deep-learning •
machine learning |
| 2 | Raufi B., Longo L. | Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios | BioMedInformatics | 2024 |
@Article{biomedinformatics4010048, AUTHOR = {Raufi, Bujar and Longo, Luca}, TITLE = {Comparing ANOVA and PowerShap Feature Selection Methods via Shapley Additive Explanations of Models of Mental Workload Built with the Theta and Alpha EEG Band Ratios}, JOURNAL = {BioMedInformatics}, VOLUME = {4}, YEAR = {2024}, NUMBER = {1}, PAGES = {853--876}, URL = {https://www.mdpi.com/2673-7426/4/1/48}, ISSN = {2673-7426}, DOI = {10.3390/biomedinformatics4010048} } [Close]
| model explainability • mental workload • statistical feature selection • Shapley-based feature selection • alpha and theta EEG band ratios • machine learning • Deep-learning |
| 1 | Lal U., Vinayak Chikkankod A., Longo L. | Fractal dimensions and machine learning for detection of Parkinson’s disease in resting-state electroencephalography | Neural Computing and Applications | 2024 | 
@article{lal2024fractal, title={Fractal dimensions and machine learning for detection of Parkinson’s disease in resting-state electroencephalography}, author={Lal, Utkarsh and Chikkankod, Arjun Vinayak and Longo, Luca}, journal={Neural Computing and Applications}, volume={36}, number={15}, pages={8257--8280}, year={2024}, publisher={Springer} } [Close]
| Electroencephalography •
Explainable AI •
Fractal dimension •
Entropy •
Sliding windowing •
Feature extraction •
Supervised learning •
Machine Learning •
Deep-learning |
| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |