| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |
|---|---|---|---|---|---|---|
| 6 | El-Qoraychy FZ, Mualla Y., Zhao H., Dridi M., Créput JC, Longo L. | Explainable AI for sign language recognition models: Integrating Grad-Cam LIME and Integrated Gradients | Plos One | 2025 |  
@article{El-QoraychyLongo2025, doi = {10.1371/journal.pone.0336481}, author = {El-Qoraychy, Fatima-Zahrae AND Mualla, Yazan AND Zhao, Hui AND Dridi, Mahjoub AND Créput, Jean-Charles AND Longo, Luca}, journal = {PLOS ONE}, publisher = {Public Library of Science}, title = {Explainable AI for sign language recognition models: Integrating Grad-Cam LIME and Integrated Gradients}, year = {2025}, month = {12}, volume = {20}, url = {https://doi.org/10.1371/journal.pone.0336481}, pages = {1-24}, number = {12} } [Close]
| Sign language • Machine Learning • Explainable Artificial Intelligence • Grad-Cam • Lime • Integrated Gradients |
| 5 | Davydko O., Pavlov V., Longo L. | A Combination of Integrated Gradients and SRFAMap for Explaining Neural Networks Trained with High-Order Statistical Radiomic Features | eXplainable Artificial Intelligence, The World Conference (xAI-2025) | 2025 | 
@InProceedings{OleksandrLongo2025, author="Davydko, Oleksandr and Pavlov, Vladimir and Longo, Luca", editor="Guidotti, Riccardo and Schmid, Ute and Longo, Luca", title="A Combination of Integrated Gradients and SRFAMap for Explaining Neural Networks Trained with High-Order Statistical Radiomic Features", booktitle="Explainable Artificial Intelligence", year="2026", publisher="Springer Nature Switzerland", address="Cham", pages="359--379", isbn="978-3-032-08317-3" } [Close]
| Explainable artificial intelligence • Radiomics • Texture analysis • Medical image processing • Saliency map • Integrated Gradients • Neural Networks • Interpretable Machine Learning |
| 4 | Marochko V., Rogala J., Longo L. | Integrated Gradients for Enhanced Interpretation of P3b-ERP Classifiers Trained with EEG-superlets in Traditional and Virtual Environments | Joint Proceedings of the xAI 2025 Late-breaking Work, Demos and Doctoral Consortium co-located with the 3rd World Conference on eXplainable Artificial Intelligence (xAI 2025) | 2025 |
@inproceedings{MarochkoLongo2025, title={Integrated Gradients for Enhanced Interpretation of P3b-ERP Classifiers Trained with EEG-superlets in Traditional and Virtual Environments}, author={Marochko, Vladimir and Rogala, Jacek and Longo, Luca}, year={2025}, booktitle = {Joint Proceedings of the xAI 2025 Late-breaking Work, Demos and Doctoral Consortium co-located with the 3rd World Conference on eXplainable Artificial Intelligence (xAI 2025), Istanbul, Turkey, 9-11 July, 2025}, publisher = {CEUR-WS.org}, volume = {4017}, series = {{CEUR} Workshop Proceedings}, editor = {Przemys?aw Biecek, Slawomir Nowaczyk, Gitta Kutyniok, Luca Longo}, pages={49-56}, url={https://ceur-ws.org/Vol-4017/paper_07.pdf} } [Close]
| Event-related potentials • Deep learning • Convolutional neural networks • Explainable Artificial Intelligence •
Integrated Gradients • P3b • Oddball paradigm • time-frequency super-resolution • Superlets. |
| 3 | Marochko V., and Longo L. | Enhancing the analysis of the P300 event-related potential with integrated gradients on a convolutional neural network trained with superlets | Joint Proceedings of the xAI 2024 Late-breaking Work, Demos and Doctoral Consortium co-located with the 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024) | 2024 | 
@inproceedings{Marochko2024, title={Enhancing the analysis of the P300 event-related potential with integrated gradients on a convolutional neural network trained with superlets}, author={Marochko, Vladimir, and Longo, Luca}, year={2024}, booktitle = {Joint Proceedings of the xAI 2024 Late-breaking Work, Demos and Doctoral Consortium co-located with the 2nd World Conference on eXplainable Artificial Intelligence (xAI 2024), Valletta, Malta, 17-19 July, 2024}, publisher = {CEUR-WS.org}, url = {https://ceur-ws.org/Vol-3793/paper_19.pdf}, volume = {3793}, series = {{CEUR} Workshop Proceedings}, editor = {Luca Longo, Weiru Liu, Grégoire Montavon}, pages={145-152} } [Close]
| Event-related potentials • Deep learning • Convolutional neural networks • Explainable Artificial Intelligence •
Integrated gradients • P3b • Oddball paradigm • time-frequency super-resolution • Superlets |
| 2 | 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 |
| 1 | Davydko O., Pavlov V., Longo L. | Selecting textural characteristics of chest X-Rays for pneumonia lesions classification with the integrated gradients XAI attribution method | eXplainable Artificial Intelligence, The World Conference (xAI-2023) | 2023 |
@InProceedings{DavydkoLongo2023, author="Davydko, Oleksandr and Pavlov, Vladimir and Longo, Luca", editor="Longo, Luca", title="Selecting Textural Characteristics of Chest X-Rays for Pneumonia Lesions Classification with the Integrated Gradients XAI Attribution Method", booktitle="Explainable Artificial Intelligence", year="2023", publisher="Springer Nature Switzerland", address="Cham", pages="671--687", isbn="978-3-031-44064-9" } [Close]
| Explainable artificial intelligence • Neural networks • Texture analysis • Medical image processing • Classification • Machine Learning |
| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |