| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |
|---|---|---|---|---|---|---|
| 4 | 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 |
| 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 | 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 |
| 1 | Davydko O., Horodetska O., Nastenko I., Hladkyi Y., Pavlov V., Linnik M., Galkin O., Longo L. | A Pipeline for the Diagnosis and Classification of Lung Lesions for Patients with COVID-19 | IEEE 17th International Conference on Computer Sciences and Information Technologies | 2022 |
@INPROCEEDINGS{10000435, author={Davydko, Oleksandr and Horodetska, Olena and Nastenko, Ievgen and Hladkyi, Yaroslav and Pavlov, Vladimir and Linnik, Mykola and Galkin, Oleksandr and Longo, Luca}, booktitle={2022 IEEE 17th International Conference on Computer Sciences and Information Technologies (CSIT)}, title={A Pipeline for the Diagnosis and Classification of Lung Lesions for Patients with COVID-19}, year={2022}, volume={}, number={}, pages={551-554}, doi={10.1109/CSIT56902.2022.10000435}} [Close]
| COVID-19 • classification • segmentation • neural network • logistic self-organized forest • texture analysis |
| # | Authors | Title | Details | Date | Pdf/Links/Bibtex | Keywords |