Marek Krętowski - The List of Publications

2024

• Czajkowska A., Czajkowski M., Szczerbinski L., Jurczuk K., Reska D., Kwedlo W., Kretowski M., Zabielski P., Kretowski A., Exploring protein relative relations in skeletal muscle proteomic analysis for insights into insulin resistance and type 2 diabetes. Scientific Reports vol. 14: 17631. (Link)
• Kretowska M., Kretowski M., Global induction of oblique survival trees, ICCS'24, Malaga, Spain. Lecture Notes in Computer Science, vol. 14835: 379–386. (ICCS2024.pdf 326 KB)
• Sokolowski H., Czajkowski M., Czajkowska A., Jurczuk K., Kretowski M., ITree: a user-driven tool for interactive decision-making with classification trees, Bioinformatics, vol. 50(5): btae273. (Link)
• Reska D., Kretowski M., GPU-accelerated lung CT segmentation based on level sets and texture analysis, Scientific Reports, vol. 14: 1444. (SR2024.pdf 3.34 MB)
• Jurczuk K., Czajkowski M., Kretowski M., Adaptive in-memory representation of decision trees for GPU-accelerated evolutionary induction, Future Generation Computer Systems, vol. 153: 419--430. (Link)

2023

• Czajkowski M., Jurczuk K., Kretowski M., Steering the interpretability of decision trees using lasso regression - an evolutionary perspective, Information Sciences, vol. 638: 118944. (Link)
• Jurczuk K., Czajkowski M., Kretowski M., From Random Forest to an interpretable decision tree - An evolutionary approach, GECCO'23, Lisbon, Portugal. GECCO 2023 Companion, pp. 291-294. (GECCO2023.pdf 999 KB)
• Czajkowski M., Jurczuk K., Kretowski M., Hierarchical relative expression analysis in multi-omics data classification, ICCS'23, Prague, Czechia. Lecture Notes in Computer Science, vol. 14074: 722–729. (ICCS2023.pdf 466 KB)
• Jurczuk K., Czajkowski M., Kretowski M., Compact in-memory representation of decision trees in GPU-accelerated evolutionary induction, PPAM'22, Gdansk, Poland. Lecture Notes in Computer Science, vol. 13826: 126-138. (PPAM2022.pdf 1.82 MB)
• Godlewski A., Czajkowski M., Mojsak P., Pienkowski T., Gosk W., Lyson T., Mariak Z., Reszec J., Kondraciuk M., Kaminski K., Kretowski M., Moniuszko M., Kretowski A., Ciborowski M., A comparison of different machine-learning techniques for the selection of a panel of metabolites allowing early detection of brain tumors, Scientific Reports, vol. 13: 11014. (Link)

2022

• Jurczuk K., Czajkowski M., Kretowski M., GPU-based acceleration of evolutionary induction of model trees, Applied Soft Computing, vol. 119: 108503. (ASC2022.pdf 1.86 MB )
• Wojciechowska G., Szczerbinski L., Kretowski M., Niemira M., Hady H.R., Kretowski A., Exploring microRNAs as predictive biomarkers for type 2 diabetes mellitus remission after sleeve gastrectomy: A pilot study, Obesity, vol. 30:435–-446. (Link)

2021

• Reska D., Czajkowski M., Jurczuk K., Boldak C., Kwedlo W., Bauer W., Koszelew J., Kretowski M., Integration of solutions and services for multi-omics data analysis towards personalized medicine, Biocybernetics and Biomedical Engineering, vol. 41: 1646–1663. (BBE2021.pdf 2.77 MB)
• Jurczuk K., Czajkowski M., Kretowski M., Multi-GPU approach to global induction of classification trees for large-scale data mining, Applied Intelligence, vol. 51: 5683–5700. (AI2021.pdf 2.17 MB)
• Czajkowski M., Jurczuk K., Kretowski M., Accelerated Evolutionary Induction of Heterogeneous Decision Trees for Gene Expression-Based Classification, GECCO'21, Lille, France. GECCO 2021 Proceedings, pp. 946-954. (GECCO2021a.pdf 1.65 MB)
• Jurczuk K., Czajkowski M., Kretowski M., Understanding evolutionary induction of decision trees: A multi-tree repository approach, GECCO'21, Lille, France. GECCO 2021 Companion, pp. 155-156. (GECCO2021b.pdf 160 KB)
• Reska D., Kretowski M., GPU-accelerated image segmentation based on level sets and multiple texture features, Multimedia Tools and Applications, vol. 80: 5087–5109. (MTAP2021.pdf 3.51 MB)
• Jurczuk K., Czajkowski M., Kretowski M., Fitness evaluation reuse for accelerating GPU-based evolutionary induction of decision trees, International Journal of High Performance Computing Applications, vol. 35(1): 20–32. (IJHPCA2021.pdf 1409 KB)

2020

• Czajkowski M., Jurczuk K., Kretowski M., Generic Relative Relations in Hierarchical Gene Expression Data Classification, PPSN'20, Leiden, Holand. Lecture Notes in Computer Science, vol. 12270: 372-384. (PPSN2020.pdf 549 KB)
• Reska D., Kretowski M., Multi-Resolution Texture-Based 3D Level Set Segmentation, IEEE Access vol. 8: 143294-305. (Access2020.pdf 1851 KB)
• Czajkowski M., Jurczuk K., Kretowski M., Tree Based Advanced Relative Expression Analysis, ICCS'20, Amsterdam, Holand. Lecture Notes in Computer Science, vol. 12139: 496-510. (ICCS2020.pdf 750 KB)
• Jurczuk K., Czajkowski M., Kretowski M., Accelerating GPU-based Evolutionary Induction of Decision Trees - Fitness Evaluation Reuse, PPAM'19, Bialystok, Poland. Lecture Notes in Computer Science, vol. 12043: 421-431. (PPAM2019b.pdf 864 KB)
• Czajkowski M., Jurczuk K., Kretowski M., Relative Expression Classification Tree. A Preliminary GPU-based Implementation, PPAM'19, Bialystok, Poland. Lecture Notes in Computer Science, vol. 12043: 359-369. (PPAM2019a.pdf 563 KB)

2009-2019

• Czajkowski M., Kretowski M., Decision Tree Underfitting in Mining of Gene Expression Data. An Evolutionary Multi-Test Tree Approach, Expert Systems With Applications vol. 137: 392-404, 2019. (ESWA2019.pdf 1845 KB)
• Czajkowski M., Kretowski M., Relative evolutionary hierarchical analysis for gene expression data classification, GECCO'19, Prague, Czech Republic. GECCO 2019 Proceedings, pp. 1156-1164, 2019. (GECCO2019a.pdf 818 KB)
• Jurczuk K., Czajkowski M., Kretowski M., Multi-GPU approach for big data mining - global induction of decision trees, GECCO'19, Prague, Czech Republic. GECCO 2019 Companion, pp. 175-176, 2019. (GECCO2019b.pdf 647 KB)
• Kretowski M., Evolutionary Decision Trees in Large-Scale Data Mining, Studies in Big Data, vol. 59, Springer, 2019.
• Czajkowski M., Kretowski M., A Multi-Objective Evolutionary Approach to Pareto Optimal Model Trees, Soft Computing vol. 23(5): 1423-1437, 2019. (SC2019.pdf 1908 KB)

• Jurczuk K., Reska D., Kretowski M., What are the limits of evolutionary induction of decision trees? PPSN'18, Coimbra, Portugal. Lecture Notes in Computer Science, vol. 11102: 461-473, 2018. (PPSN2018.pdf 638 KB)
• Jurczuk K.,Kretowski M., Bezy-Wendling J., GPU-based computational modeling of magnetic resonance imaging of vascular structures, Int. Journal of High Performance Computing Applications, vol. 32(4): 496–511, 2018. (IJHPCA2018.pdf 1772 KB)
• Reska D., Jurczuk K., Kretowski M., Evolutionary Induction of Classification Trees on Spark, ICAISC'18, Zakopane, Poland. Lecture Notes in Artificial Intelligence, vol. 10841: 514-523, 2018. (ICAISC2018.pdf 446 KB)
• Simoncini C., Jurczuk K., Reska D., Esneault S., Nunes J., Bellanger J., Saint-Jalmes H., Rolland Y., Eliat P., Bezy-Wendling J., Kretowski M., Towards a patient-specific hepatic arterial modeling for microspheres distribution optimisation in SIRT protocol, Medical & Biological Engineering & Computing , vol. 56: 515-529, 2018. (MBEC2018.pdf 8639 KB)
• Kretowski M., Czajkowski M., Evolutionary Algorithms for Global Decision Tree Induction, Encyclopedia of Information Science and Technology, Fourth Edition, 2132-2141, 2018.

• Jurczuk K., Czajkowski M., Kretowski M., GPU-accelerated evolutionary induction of regression trees, TPNC '17, Prague, Czech Republic. Lecture Notes in Computer Science, vol. 10687: 87-99, 2017. (TPNC2017.pdf 463 KB)
• Simoncini C., Rolland Y., Morgenthaler V., Jurczuk K., Saint-Jalmes H., Eliat P.-A., Kretowski M., Bezy-Wendling J., Blood Flow Simulation in Patient-Specific Segmented Hepatic Arterial Tree, IRBM - Innovation and Research in BioMedical Engineering, vol. 38(3): 120-126, 2017. (IRBM2017.pdf 1322 KB)
• Jurczuk K., Czajkowski M., Kretowski M., Evolutionary Induction of Decision Tree for Large Scale Data. A GPU-based Approach, Soft Computing, vol. 21: 7363-79, 2017. (SC2017.pdf 1276 KB)
• Reska D., Boldak C., Kretowski M., Towards multi-stage texture based active contour image segmentation. Signal, Image and Video Processing, vol. 11(5): 809-816, 2017. (SIVP2017.pdf 2173 KB)

• Czajkowski M., Kretowski M., A Multi-Objective Evolutionary Approach to Pareto Optimal Model Trees. A Preliminary Study, TPNC '16, Sendai, Japan. Lecture Notes in Computer Science, vol. 10071: 85-96, 2016. (TPNC2016.pdf 1016 KB)
• Czajkowski M., Czajkowska A., Kretowski M., TIGER: an evolutionary search for Top Inter-GEne Relations. International Journal of Data Mining and Bioinformatics, vol. 16(2): 170-182, 2016. (IJDMB2016.pdf 602 KB)
• Duda D., Kretowski M., Azzabou N., de Certaines J., MRI Texture-Based Classification of Dystrophic Muscles. A Search for the Most Discriminative Tissue Descriptors, CISIM 2016. Lecture Notes in Computer Science, vol. 9842: 116-128, 2016.  (CISIM2016.pdf 330 KB)
• Czajkowski M., Jurczuk K., Kretowski M., Hybrid Parallelization of Evolutionary Model Tree Induction, ICAISC'16, Zakopane, Poland. Lecture Notes in Artificial Intelligence, vol. 9692: 370-379, 2016. (ICAISC2016.pdf 718 KB)
• Duda D., Kretowski M., Mathieu R., de Crevoisier R., Bezy-Wendling J., Multi-image texture analysis in classiffication of prostatic tissues from MRI. Biocybernetics and Biomedical Engineering, vol. 36(4): 537-552, 2016. (BBE2016.pdf 1396 KB)
• Jurczuk K., Murawski D., Kretowski M., Bezy-Wendling J., GPU Accelerated Simulations of Magnetic Resonance Imaging of Vascular Structures, PPAM'15, Cracow, Poland, Lecture Notes in Computer Science, vol. 9573: 389-398, 2016. (PPAM2015.pdf 1110 KB)
• Czajkowski M., Kretowski M., The Role of Decision Tree Representation in Regression Problem - Evolutionary Perspective, Applied Soft Computing, vol. 48: 458-475, 2016. (ASC2016.pdf 4285 KB)

• Reska D., Boldak C., Kretowski M., Toward texture-based 3D level set image segmentation, IP&C'15, Bydgoszcz, Poland. Image Processing and Communications Challenges 7. Advances in Intelligent Systems and Computing, vol. 389: 205-211, 2015. (IPCC2015b.pdf 306 KB)
• Duda D., Kretowski M., Azzabou N., de Certaines J., MRI texture analysis for differentiation between healthy and Golden Retriever Muscular Dystrophy dogs at different phases of disease evolution, CISIM 2015. Lecture Notes in Computer Science, vol. 9339: 255-266, 2015. (CISIM2015.pdf 271 KB)
• Czajkowski M., Czerwonka M., Kretowski M., Cost-sensitive Global Model Trees applied to loan charge-off forecating,  Decision Support Systems, vol. 74: 57-66, 2015. (DSS2015.pdf 1071 KB)
• Czajkowski M., Jurczuk K., Kretowski M., A Parallel Approach for Evolutionary Induced Decision Trees. MPI+OpenMP Implementation, ICAISC'15, Zakopane, Poland. Lecture Notes in Artificial Intelligence, vol. 9119: 340-349, 2015. (ICAISC2015.pdf 361 KB)
• Reska D., Boldak C., Kretowski M., A texture-based energy for active contour image segmentation, IP&C'14, Bydgoszcz, Poland. Image Processing and Communications Challenges 6. Advances in Intelligent Systems and Computing, vol. 313: 187-194, 2015. (IPCC2015a.pdf 517 KB)

• Jurczuk K., Kretowski M., Eliat P.-A., Saint-Jalmes H., Bezy-Wendling J., In Silico Modeling of Magnetic Resonance Flow Imaging in Complex Vascular Networks, IEEE Transactions on Medical Imaging, vol. 33 (11): 2191-2209, 2014. (IEEE_TMI_2014.pdf 2387 KB)
• Czajkowski M., Kretowski M.,  Evolutionary Induction of Global Model Trees with Specialized Operators and Memetic Extensions, Information Sciences, vol. 288: 153-173, 2014. (IS2014.pdf 1632 KB)
• Reska D., Jurczuk K., Boldak C., Kretowski M., MESA: Complete approach for design and evaluation of segmentation methods using real and simulated tomographic images. Biocybernetics and Biomedical Engineering, vol. 34: 146-158, 2014. (BBE2014.pdf 2762 KB)
• Boldak C., Reska D., Kretowski M., New deformable models development using the MESA environment. Journal of Applied Computer Science, vol. 22(1): 29-48, 2014. (JACS2014.pdf 650 KB)
• Czajkowski M., Grześ M., Kretowski M., Multi-Test Decision Tree with Application in Microarray Data Classification, Artificial Intelligence in Medicine, vol. 61(1): 35-44, 2014. (AIM2014.pdf 995 KB)
• Kretowski M., Czajkowski M., Global Induction of Classification and Regression Trees, Encyclopedia of Business Analytics and Optimization, 1080-1089, 2014.
• Duda D., Kretowski M., Mathieu R., de Crevoisier R., Bezy-Wendling J., Multi-image texture analysis in classiffication of prostatic tissues from MRI. Preliminary results, ITB 2014, Kamien Slaski, Poland. Information Technologies in Biomedicine 3, Advances in Intelligent and Soft Computing, vol. 283: 139-150, 2014. (ITIB2014.pdf 754 KB)
• Czajkowski M., Kretowski M., Evolutionary Approach for Relative Gene Expression Algorithms, The Scientific World Journal, vol. 2014: Article ID 593503, 2014. (SWJ2014.pdf 211 KB)
• Reska D., Boldak C., Kretowski M., A distributed approach for development of deformable model-based segmentation methods, IP&C'13, Bydgoszcz, Poland. Image Processing and Communications Challenges 5. Advances in Intelligent Systems and Computing, vol. 233: 21-28, 2014. (IPCC2014.pdf 617 KB)
• Czajkowski M., Czerwonka M., Kretowski M., Cost-Sensitive Extensions for Global Model Trees. Application in Loan Charge-Off Forecasting,  ICSS 2013, Wroclaw, Poland. Advances in Systems Science. Advances in Intelligent Systems and Computing, vol. 240: 315-324, 2014. (ICSS2013.pdf 262 KB)

• Duda D., Kretowski M., Bezy-Wendling J. A computer-aided diagnosis of liver tumors based on multi-image texture analysis of contrast-enhanced CT. Selection of the most appropriate texture features, Studies in Logic, Grammar and Rhetoric, vol. 35(48): 49-70, 2013. (SLGR2013.pdf 612 KB)
• Duda D., Kretowski M., Bezy-Wendling J., Effect of Slice Thickness on Texture-Based Classification of Liver Dynamic CT Scans, CISIM'13, Cracow, Poland. Lecture Notes in Computer Science, vol. 8104: 84-95, 2013. (CISIM2013.pdf 328 KB)
• Jurczuk K., Kretowski M., Bellanger J.-J., Eliat P.-A., Saint-Jalmes H., Bezy-Wendling J., Computational modeling of MR flow imaging by the lattice Boltzmann method and Bloch equation, Magnetic Resonance Imaging, vol. 31: 1163-1173, 2013. (MRI2013.pdf 2034 KB)
• Czajkowski M., Kretowski M., An Evolutionary Algorithm for Global Induction of Regression and Model Trees, International Journal of Data Mining, Modelling and Management, vol. 5(3): 261-276, 2013. (IJDMMM2012.pdf 297 KB) 
• Czajkowski M., Kretowski M., Global Induction of Oblique Model Trees: An Evolutionary Approach, ICAISC'13, Zakopane, Poland. Lecture Notes in Artificial Intelligence, vol. 7895: 1-11, 2013. (ICAISC2013.pdf 493 KB)
• Czajkowski M., Kretowski M., Global Top-Scoring Pair Decision Tree for Gene Expression Data Analysis, EuroGP'13, Vienna, Austria. Lecture Notes in Computer Science, vol. 7831: 229-240, 2013. (EuroGP2013.pdf 254 KB)

• Reska D., Boldak C., Kretowski M., Fast 3D Segmentation of Hepatic Images Combining Region and Boundary Criteria, Image Processing & Communication, vol. 17 (4): 31-38, 2012. (IPC12.pdf 2.07 MB)
• Jurczuk K., Kretowski M., Bezy-Wendling J., Hierarchical Parallel Approach in Vascular Network Modeling - Hybrid MPI+OpenMP Implementation,  PPAM 2011, Torun, Poland. Lecture Notes in Computer Science, vol. 7203: 376-385, 2012. (PPAM2011.pdf 425 KB)
• Jurczuk K., Kretowski M., Eliat P.-A., Bellanger J.-J., Saint-Jalmes H., Bezy-Wendling J. A New Approach in Combined Modeling of MRI and Blood Flow: A Preliminary Study, IEEE ISBI'12, Barcelona, Spain, pp. 812-815, 2012. (ISBI2012.pdf 571 KB)
• Czajkowski M., Kretowski M., Does Memetic Approach Improve Global Induction of Regression and Model Trees? Swarm and Evolutionary Computation, ICAISC'12, Zakopane, Poland. Lecture Notes in Computer Science, vol. 7269: 174-181, 2012. (ICAISC2012.pdf 323 KB)

• Reska D., Krętowski M., HIST - An application for segmentation of hepatic images, Zeszyty Naukowe Politechniki Białostockiej. Informatyka, vol. 7: 71-93, 2011. (ZNPBI2011.pdf 1.63 MB)
• Czajkowski M., Grześ M., Kretowski M., Multi-Test Decision Trees for Gene Expression Data Analysis, S&IIS'11, Warsaw, Poland. Lecture Notes in Computer Science, vol. 7043: 154-167, 2011. (SIIS2011.pdf 270 KB)
• Jurczuk K., Kretowski M., Bezy-Wendling J., Vascular System Modeling in Parallel Environment - Distributed and Shared Memory Approaches, IEEE Transactions on Information Technology in Biomedicine,  vol. 15 (4): 668-672, 2011. (IEEE_TITB_2011.pdf 512 KB) 
• Czajkowski M., Kretowski M.,  An Evolutionary Algorithm for Global Induction of Regression Trees with Multivariate Linear Models, ISMIS'11, Warsaw, Poland. Lecture Notes in Artificial Intelligence, vol. 6804: 230-239, 2011. (ISMIS2011.pdf 252 KB)
• Czajkowski M., Krętowski M., Top Scoring Pair Decision Tree for Gene Expression Data Analysis, In: Arabnia H., Tran Q. (Eds.) Software Tools and Algorithms for Biological Systems, Advances in Experimental Medicine and Biology, vol. 696: 27-35, 2011. (AEMB2011.pdf 212 KB) 

• Czajkowski M., Krętowski M.,  Globally Induced Model Trees: An Evolutionary Approach, PPSN XI, Cracow, Poland. Lecture Notes in Computer Science, vol. 6238: 324-333, 2010. (PPSN2010.pdf 252 KB)
• Jurczuk K., Krętowski M., Bezy-Wendling J., Load balancing in parallel implementation of vascular network modeling, Zeszyty Naukowe Politechniki Białostockiej. Informatyka, vol. 6: 41-62, 2010. 
• Krętowski M., Czajkowski M., An Evolutionary Algorithm for Global Induction of Regression Trees, ICAISC'10, Zakopane, Poland. Lecture Notes in Artificial Intelligence, vol. 6114: 157-164, 2010. (ICAISC2010.pdf 300 KB) 
• Jurczuk K., Kretowski M., Bezy-Wendling J., Vascular network modeling – Improved parallel implementation on computing cluster, PPAM'09, Wroclaw, Poland. Lecture Notes in Computer Science, vol. 6067: 289-298, 2010.  (PPAM2009.pdf 414 KB)
• Mescam M., Kretowski M., Bezy-Wendling J., Multiscale model of liver DCE-MRI towards a better understanding of tumor complexity, IEEE Transactions on Medical Imaging, vol. 29 (3): 699-707, 2010. (IEEE_TMI_2010.pdf 1322 KB)

• Czajkowski M., Krętowski M., An extension of TSP-family algorithms for microarray classification, Zeszyty Naukowe Politechniki Białostockiej. Informatyka, vol. 4: 31-45, 2009. (ZNPBI2009.pdf 527 KB)
• Jurczuk K., Krętowski M., Virtual magnetic resonance imaging – parallel implementation in a cluster computing environment, Biocybernetics and Biomedical Engineering, vol. 29 (3): 31-46, 2009. (BBE2009.pdf 6.49 MB)
• Mescam M., Krętowski M., Bezy-Wendling J., Texture-based characterization of arterialization in simulated MRI of hypervasculaized liver tumors, IEEE ISBI'09, Boston, USA, pp. 93-96, 2009.

2003-2008

• Krętowski M., Grześ M., Global induction of decision trees. (Invited short chapter) In: Wang J. (Ed.) Encyclopedia od Data Warehousing and Mining, Second Edition, Information Science Reference, vol. II: 937-942, 2008.
• Krętowski M., Obliczenia ewolucyjne w eksploracji danych. Globalna indukcja drzew decyzyjnych, Wydawnictwo Politechniki Białostockiej, 2008. (in polish)
• Bezy-Wendling J., Krętowski M., Physiological Modeling of Tumor-affected Renal Circulation. Computer Methods and Programs in Biomedicine, vol. 91(1): 1-12, 2008. (CMPB2008.pdf 5.06 MB)
• Jurczuk K., Krętowski M., Parallel implementation of vascular network modeling, ICCS'08, Krakow, Poland. Lecture Notes in Computer Science, vol. 5101: 679-688, 2008. (ICCS2008.pdf 935 KB)
• Krętowski M., Popczyński P., Global Induction of Decision Trees: From Parallel Implementation to Distributed Evolution, ICAISC'08, Zakopane, Poland. Lecture Notes in Artificial Intelligence, vol. 5097: 426-437, 2008. (ICAISC2008.pdf 508 KB).
• Czajkowski M., Krętowski M., Novel extension of k-TSP algorithm for micro-array classification, IEA-AIE'08, Wroclaw, Poland. Lecture Notes in Artificial Intelligence, vol. 5027: 456-465, 2008. (IEAAIE2008.pdf 1.79 MB)
• Krętowski M., A Memetic Algorithm for Global Induction of Decision Trees. SOFSEM'08, Novy Smokovec, Slovakia. Lecture Notes in Computer Science, vol. 4910: 531-540, 2008. (SOFSEM2008.pdf 441 KB)

• Duda D., Krętowski M., Bezy-Wendling J., Ekstrakcja cech teksturalnych w klasyfikacji obrazów tomograficznych wątroby, Zeszyty Naukowe Politechniki Białostockiej. Informatyka, vol. 2: 51-66, 2007. (in polish)
• Krętowski M., Grześ M., Evolutionary Induction of Mixed Decision Trees, International Journal of Data Warehousing and Mining, vol. 3(4): 68-82, 2007. (IJDWM2007.pdf 751 KB)
• Grześ M., Krętowski M., Decision Tree Approach to Microarray Data Analysis, Biocybernetics and Biomedical Engineering, vol. 27(3): 29-42, 2007. (BBE2007.pdf 1074 KB)
• Mescam M., Bezy-Wendling J., Krętowski M., Jurczuk K., Eliat P.-A., Olivie D., Coupling texture analysis and physiological modeling for liver dynamic MRI interpretation, IEEE EMBS'07, Lyon, France, pp. 4223-4226, 2007.
• Bezy-Wendling J., Krętowski M., Mescam M., Jurczuk K., Eliat P.-A., Simulation of Hepatocellular Carcinoma in MRI by Combined Macrovascular and Pharmacokinetic Models, IEEE ISBI'07, Washington, USA, pp. 1272-1275, 2007.
• Krętowski M., Grześ M., Evolutionary Induction of Decision Trees for Misclassification Cost Minimization, ICANNGA'07, Warsaw, Poland. Lecture Notes in Computer Science, vol. 4431: 1-10, 2007. (ICANNGA2007.pdf 403 KB)
• Krętowski M., Bezy-Wendling J., Coupe P., Simulation of Biphasic CT Findings in Hepatic Cellular Carcinoma by a Two-level Physiological Model, IEEE Transactions on Biomedical Engineering, vol. 54 (3): 538-542, 2007. (IEEE_TBME.pdf 2.64 MB)

• Duda D., Krętowski M., Bezy-Wendling J., Texture Characterization for Hepatic Tumor Recognition in Multiphase CT, Biocybernetics and Biomedical Engineering, vol. 26(4): 15-24, 2006. (BBE2006.pdf 116 KB)
• Krętowski M., Grześ M., Evolutionary Induction of Cost-Sensitive Decision Trees, ISMIS'06, Bari, Italy. Lecture Notes in Artificial Intelligence, vol. 4203: 121-126, 2006. (ISMIS2006.pdf 142 KB)
• Krętowski M., Grześ M., Mixed Decision Trees: An Evolutionary Approach, DaWaK'06, Cracow, Poland. Lecture Notes in Computer Science, vol. 4081: 260-269, 2006. (DaWaK2006.pdf 628 KB)
• Bezy-Wendling J., Eliat P.-A., Szekely G., Collewet G., Benoit-Cattin H., Krętowski M., de Certaines J., Modelling: from living body to MRI. In: Hajek M., Dezortova M., Materka A., R. Lerski (Eds.) Texture Analysis for Magnetic Resonance Imaging, Med4publishing, 45-79, 2006.
• Krętowski M., Grześ M., Evolutionary Learning of Linear Trees with Embedded Feature Selection, ICAISC'06, Zakopane, Poland. Lecture Notes in Artificial Intelligence, vol. 4029: 400-409, 2006. (ICAISC2006.pdf 459 KB)

• Krętowski M., Bezy-Wendling J., Modeling for Medical Image Analysis: Framework and Applications. (Invited chapter) In: Leondes C. (Ed.) Medical Imaging Systems Technology vol. I: Analysis and Computational Methods, World Scientific, 1-32, 2005. (MIST.pdf 5.85 MB)
• Krętowski M., Bezy-Wendling J., Parafianowicz K., Modelowanie rozwoju sieci naczyń krwionośnych w narządach wewnętrznych, Proc. of XIV National Conference Biocybernetics and Biomedical Engineering, 2005. (in polish)
• Duda D., Krętowski M., Bezy-Wendling J., Klasyfikacja tekstur w rozpoznawaniu nowotworów wątroby na podstawie serii obrazów tomograficznych, Proc. of XIV National Conference Biocybernetics and Biomedical Engineering, 2005. (in polish) (KBIB2005.pdf 180 KB)
• Krętowski M., Grześ M., Global Learning of Decision Trees by an Evolutionary Algorithm. In: Information Processing and Security Systems. Springer, 401-410, 2005. (IPSS.pdf 627 KB)
• Duda D., Krętowski M., Bezy-Wendling J., Texture analysis in medical image classification, Statistics and Clinical Practice, Warsaw, Poland. Lecture Notes of ICB Seminars, vol. 70: 83-89, 2005.
• Grześ M., Krętowski M., Decision tree approach for microarray data analysis, Statistics and Clinical Practice, Warsaw, Poland. Lecture Notes of ICB Seminars, vol. 70: 105-111, 2005.
• Krętowski M., Grześ M., Global induction of oblique decision trees: An evolutionary approach, Intelligent Information Processing and Web Mining. Proc. of the IIS 2005, Gdańsk, Poland. Springer, Advances in Soft Computing, 309-318, 2005. (IIPWM2005.pdf 190 KB)

• Duda D., Krętowski M., Wykorzystanie SVM w rozpoznawaniu tekstur, Proc. of 11th Workshop Simulation in Research and Development. Augustów, Poland, 2004. (in polish).
• Duda D., Krętowski M., Bezy-Wendling J., Texture-Based Classification of Hepatic Primary Tumors in Multiphase CT, MICCAI'04, St. Malo, France. Lecture Notes in Computer Science, vol. 3217: 1050-1051, 2004. (MICCAI2004.pdf 64 KB)
• Krętowski M., Grześ M., An evolutionary algorithm for global induction of decision trees, ACS-CISIM'04, Ełk, Poland, 2004.
• Krętowski M., An evolutionary algorithm for oblique decision tree induction. ICAISC'04, Zakopane, Poland. Lecture Notes in Artificial Intelligence, vol. 3070: 432-437, 2004. (ICAISC2004.pdf 276 KB)
• Bezy-Wendling J., Krętowski M., Coupe P., Modeling of Tumor Conspicuity in Hepatic CT Images: Combined Compartment and Vascular Models, Proc. of IEEE International Symposium on Biomedical Imaging ISBI'04, Arlington, USA, 2004.
• Krętowski M., Bezy-Wendling J., Computer modelling of vascular systems. TASK Quarterly (Special issue entitled "Computers in Medical Applications"), vol. 8(2): 223-229, 2004.

• Krętowski M., Bezy-Wendling J., Duda D., Classification of hepatic metastasis in enhanced CT images by dipolar decision tree. Proc. of XIX GRETSI Conference, Paris, France, 2003. (GRETSI2003.pdf 159 KB)
• Krętowski M., Bezy-Wendling J., Komputerowe modelowanie systemów naczyń krwionośnych. Proc. of XIII National Conference Biocybernetics and Biomedical Engineering, Gdańsk, Poland, 2003. (in polish)
• Kretowski M., Rolland Y., Bezy-Wendling J., Coatrieux J.-L., Physiologically based modeling of 3-D vascular networks and CT scan angiography. IEEE Transactions on Medical Imaging, vol. 22(2): 248-257, 2003. (IEEE_TMI.pdf 1.1 MB)
• Bezy-Wendling J., Krętowski M., Rolland Y., Hepatic Tumor Enhancement in Computed Tomography: Combined Models of Liver Perfusion and Dynamic Imaging. Computers in Biology and Medicine, vol. 33(1): 77-89, 2003. (CBM.pdf 2.98 MB)
• Krętowski M., Bezy-Wendling J., Duda D., Liver metastasis classification from CT images based on texture analysis. Proc. of Forum JCGBM, Nantes, France, 2003.
• Krętowski M., Bezy-Wendling J., Vascular texture modeling for image interpretation. Biocybernetics and Biomedical Engineering, vol. 23(1): 65-79, 2003. Download compressed pdf (bbe.zip 2188 KB)
• Krętowski M., Rolland Y., Bezy-Wendling J., Coatrieux J.-L., Fast 3D Modeling of Vascular Trees. Computer Methods and Programs in Biomedicine, vol. 70(2): 129-136, 2003. (CPMB.pdf 274 KB)

1995-2002

• Krętowski M., Bobrowski L., Generowanie wielowymiarowych drzew decyzyjnych na podstawie zbiorów danych. Zeszyty Naukowe Politechniki Białostockiej - Informatyka,vol. 1: 119-146, 2002. (in polish) Download compressed pdf (znpb.zip 580 KB)
• Bezy-Wendling J., Krętowski M., Modeling hepatic enhancement in computer tomography for texture interpretation, Proc. of IEEE International Symposium on Biomedical Imaging ISBI'02, Washingthon, USA, 2002.
• Kwedlo W., Krętowski M., Learning Decision Rules using a Distributed Evolutionary Algorithm. TASK Quarterly, vol.. 6 (3): 483-492, 2002. (TQ2002.pdf 461KB)
• Krętowski M., Duda D., Dipolowe drzewa decyzyjne w klasyfikacji obrazów na podstawie cech teksturalnych. Proc. of 9th Workshop Simulation in Research and Development. Koszalin-Osieki, Poland, 2002. (in polish)
• Kwedlo W., Krętowski M., An Evolutionary Algorithm for Cost-Sensitive Decision Rule Learning. European Conference on Machine Learning, ECML'01. Freiburg, Germany. Springer LNCS 2167, 2001. (ECML2001.pdf 165 KB)
• Kwedlo W., Krętowski M., Learning Decision Rules using a Distributed Evolutionary Algorithm. Proc. of 8th Workshop Simulation in Research and Development. Gdańsk, Poland, 2001.
• Bezy-Wendling J., Krętowski M., Rolland Y., Le Bidon W., Toward a Better Understanding of Texture in Vascular CT Scan Simulated Images. IEEE Transactions on Biomedical Engineering, vol. 48 (1): 120-124, 2001. (IEEE_TBME_2001.pdf 125 KB)  
• Bobrowski L., Krętowski M., Induction of Multivariate Decision Trees by using Dipolar Criteria. Principles of Data Mining and Knowledge Discovery, PKDD'00. Lyon, France. Springer LNCS 1910, 2000. (PKDD2000.pdf 121 KB)
• Kwedlo W., Krętowski M., Cost-sensitive inducton of decision rules. Proc. of the 9th Workshop Intelligent Information Systems.Bystra, Poland, 2000.
• Kwedlo W., Krętowski M., An Evolutionary Algorithm Using Multivariate Discretization for Decision Rule Induction. Principles of Data Mining and Knowledge Discovery, PKDD'99. Prague, Czech Republic. Springer LNCS 1704, 1999. (PKDD1999.pdf  146 KB)
• Kwedlo W., Krętowski M., An evolutionary algorithm integrating discretization of continuous-valued attributes with learning decision rules. Proc. of the 8th Workshop Intelligent Information Systems.Ustron, Poland, 1999.
• Bobrowski L., Krętowska M., Krętowski M., Generowanie sieci neuropodobnych oraz drzew decyzyjnych w oparciu o kryterium dipolowe. Proc. of 5th Workshop Simulation in Research and Development. Jelenia Góra, Poland, 1998. (in polish)
• Kwedlo W., Krętowski M., Discovery of decision rules from databases: an evolutionary approach. Principles of Data Mining and Knowledge Discovery, PKDD'98. Nantes, France. Springer LNCS 1510, 1998. (PKDD1998.pdf  445 KB)
• Kwedlo W., Krętowski M., Learning decision rules using an evolutionary algorithm and entropy-based discretization. Proc. of the 7th Workshop Intelligent Information Systems. Malbork, Poland, 1998.
• Bobrowski L., Krętowska M., Krętowski M., Design of neural classifying networks by using dipolar criterions, Proc. of the 3rd Conf. Neural Networks and Their Applications, Kule, Poland, 1997.
• Krętowski M., Stepaniuk J., Selection of Objects and Attributes. A Tolerance Rough Set Approach. Proc. of the Poster Session IX Int. Symposium on Methodologies for Intelligent Systems, ISMIS’96 , Zakopane, Poland, 1996. (ISMIS96pdf  470 KB)  
• Krętowski M., Polkowski L., Skowron A., Stepaniuk J., Data Reduction Based on Rough Set Theory, Proc. of the Int. Workshop on Statistics, Machine Learning and Knowledge Discovery in Databases, Crete, Greece, 1995.
• Seredyński F., Kwedlo W., Krętowski M., An epsilon automata based multi-agent system for the dynamic mapping problem. Proc. of the First Int. Workshop on Decentralised Intelligent and Multi-Agent Systems DIMAS-95. Cracow, Poland, 1995.
• Stepaniuk J., Krętowski M., Decision System Based on Tolerance Rough Sets, Proc. of the 4th Workshop Intelligent Information Systems, Augustów, Poland, 1995. (IIS95.pdf  655 KB)  

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Revised: 17-01-2024