Stebėti
Mateusz Krzyziński
Mateusz Krzyziński
Snowflake
El. paštas nepatvirtintas - Pagrindinis puslapis
Pavadinimas
Cituota
Cituota
Metai
SurvSHAP(t): Time-dependent explanations of machine learning survival models
M Krzyziński, M Spytek, H Baniecki, P Biecek
Knowledge-Based Systems 262, 110234, 2023
822023
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
132023
Amelanotic Uveal Melanomas Evaluated by Indirect Ophthalmoscopy Reveal Better Long-Term Prognosis Than Pigmented Primary Tumours—A Single Centre Experience
A Markiewicz, P Donizy, M Nowak, M Krzyziński, M Elas, PM Płonka, ...
Cancers 14 (11), 2753, 2022
122022
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
112022
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
62024
Coloring squares of planar graphs with small maximum degree
M Krzyziński, P Rzążewski, S Tur
Discussiones Mathematicae Graph Theory, 2022
62022
Interpretable machine learning for survival analysis
SH Langbein, MĹ Spytek, H Baniecki, PĹ Biecek, MN Wright
arXiv preprint arXiv:2403.10250, 2024
52024
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
42024
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
42024
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
32024
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
32022
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
22024
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
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