SurvSHAP(t): Time-dependent explanations of machine learning survival models M Krzyziński, M Spytek, H Baniecki, P Biecek Knowledge-Based Systems 262, 110234, 2023 | 82 | 2023 |
survex: an R package for explaining machine learning survival models M Spytek, M Krzyziński, SH Langbein, H Baniecki, MN Wright, P Biecek Bioinformatics 39 (12), btad723, 2023 | 13 | 2023 |
Amelanotic Uveal Melanomas Evaluated by Indirect Ophthalmoscopy Reveal Better Long-Term Prognosis Than Pigmented Primary TumoursA Single Centre Experience A Markiewicz, P Donizy, M Nowak, M Krzyziński, M Elas, PM Płonka, ... Cancers 14 (11), 2753, 2022 | 12 | 2022 |
Machine learning models demonstrate that clinicopathologic variables are comparable to gene expression prognostic signature in predicting survival in uveal melanoma P Donizy, M Krzyzinski, A Markiewicz, P Karpinski, K Kotowski, A Kowalik, ... European Journal of Cancer 174, 251-260, 2022 | 11 | 2022 |
Exploration of the Rashomon Set Assists Trustworthy Explanations for Medical Data K Kobylińska, M Krzyziński, R Machowicz, M Adamek, P Biecek IEEE Journal of Biomedical and Health Informatics 28 (11), 6454-6465, 2024 | 7* | 2024 |
Performance is not enough: the story told by a Rashomon quartet P Biecek, H Baniecki, M Krzyziński, D Cook Journal of Computational and Graphical Statistics, 1-6, 2024 | 7* | 2024 |
treeshap: Compute SHAP values for your tree-based models using the TreeSHAP algorithm K Komisarczyk, P Kozminski, S Maksymiuk, P Biecek R package, 2024 | 6 | 2024 |
Coloring squares of planar graphs with small maximum degree M Krzyziński, P Rzążewski, S Tur Discussiones Mathematicae Graph Theory, 2022 | 6 | 2022 |
Interpretable machine learning for survival analysis SH Langbein, MĹ Spytek, H Baniecki, PĹ Biecek, MN Wright arXiv preprint arXiv:2403.10250, 2024 | 5 | 2024 |
Ki67 is a better marker than PRAME in risk stratification of BAP1-positive and BAP1-loss uveal melanomas P Donizy, M Spytek, M Krzyziński, K Kotowski, A Markiewicz, ... British Journal of Ophthalmology 108 (7), 1005-1010, 2024 | 4 | 2024 |
A novel radiomics approach for predicting TACE outcomes in hepatocellular carcinoma patients using deep learning for multi-organ segmentation K Bartnik, M Krzyziński, T Bartczak, K Korzeniowski, K Lamparski, ... Scientific Reports 14 (1), 14779, 2024 | 4 | 2024 |
SATB2, CKAE1/AE3, and synaptophysin as a sensitive immunohistochemical panel for the detection of lymph node metastases of Merkel cell carcinoma A Szumera-Cieckiewicz, D Massi, A Cassisa, M Krzyzinski, ... Virchows Archiv 484 (4), 629-636, 2024 | 3 | 2024 |
Climate Policy Tracker: Pipeline for automated analysis of public climate policies A Żółkowski, M Krzyziński, P Wilczyński, S Giziński, E Wiśnios, B Pieliński, ... NeurIPS 2022 Workshop: Tackling Climate Change with Machine Learning, 2022 | 3 | 2022 |
WAW-TACE: A Hepatocellular Carcinoma Multiphase CT Dataset with Segmentations, Radiomics Features, and Clinical Data K Bartnik, T Bartczak, M Krzyziński, K Korzeniowski, K Lamparski, ... Radiology: Artificial Intelligence 6 (6), e240296, 2024 | 2 | 2024 |
Explaining and visualizing black-box models through counterfactual paths B Pfeifer, M Krzyzinski, H Baniecki, A Saranti, A Holzinger, P Biecek arXiv preprint arXiv:2307.07764, 2023 | 1* | 2023 |
Co-targeting of DTYMK and PARP1 as a potential therapeutic approach in uveal melanoma S Oziębło, J Mizera, A Górska, M Krzyziński, P Karpiński, A Markiewicz, ... Cells 13 (16), 1348, 2024 | | 2024 |
HADES: Homologous Automated Document Exploration and Summarization P Wilczyński, A Żółkowski, M Krzyziński, E Wiśnios, B Pieliński, S Giziński, ... arXiv preprint arXiv:2302.13099, 2023 | | 2023 |