@inproceedings{MTMT:36188485,
	title = {Artificial noise injection for enhancing synthetic data quality for cell counting applications},
	url = {https://m2.mtmt.hu/api/publication/36188485},
	author = {Patakvölgyi, Vivien Roxána and Kovács, Levente and Drexler, Dániel András},
	booktitle = {2025 IEEE 29th International Conference on Intelligent Engineering Systems (INES)},
	doi = {10.1109/INES67149.2025.11078023},
	unique-id = {36188485},
	year = {2025},
	pages = {65-69},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@article{MTMT:36302848,
	title = {Mixed-effects Model Fitting Based on in vivo Data from Animal Experiments},
	url = {https://m2.mtmt.hu/api/publication/36302848},
	author = {Puskás, Melánia and Gombos, Balázs and Kovács, Levente and Drexler, Dániel András},
	doi = {10.12700/APH.22.10.2025.10.4},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {22},
	unique-id = {36302848},
	issn = {1785-8860},
	abstract = {The use of mixed-effects models is becoming increasingly common in the analysis of biomedical data. They are often employed for estimating physiological parameters, where we can distinguish between fixed and random effects, and they work particularly well for analyzing similar types of data. We apply mixed-effect modeling on experimental data, in which mice with breast cancer were treated with chemotherapeutic drugs. The mice are genetically identical, they had the same type of tumor, and they were treated with the same drug. The available data are the tumor volumes at specific time points and the doses of the injected drug. We fit our mathematical model to these experimental data; the parameters of that mathematical model are essentially the physiological parameters whose estimation is crucial for optimizing the therapy. To estimate the parameters, we apply a nonlinear mixed-effects model fitting to in vivo data. © 2025, Budapest Tech Polytechnical Institution. All rights reserved.},
	keywords = {Animal experiments; model fitting; Mixed-effects model; parameter estimatio},
	year = {2025},
	eissn = {1785-8860},
	pages = {47-64},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@article{MTMT:36063808,
	title = {Modeling the error of caliper measurements in animal experiments},
	url = {https://m2.mtmt.hu/api/publication/36063808},
	author = {Puskás, Melánia and Drexler, Dániel András},
	doi = {10.1109/ACCESS.2025.3555148},
	journal-iso = {IEEE ACCESS},
	journal = {IEEE ACCESS},
	volume = {13},
	unique-id = {36063808},
	year = {2025},
	eissn = {2169-3536},
	pages = {54836-54852}
}

@inproceedings{MTMT:36157942,
	title = {Systematic Errors in Tumor Volume Estimation: Noise Modeling in Digital Caliper Measurements},
	url = {https://m2.mtmt.hu/api/publication/36157942},
	author = {Puskás, Melánia and Ferenci, Tamás and Kovács, Levente and Drexler, Dániel András},
	booktitle = {19th IEEE International Symposium on Applied Computational Intelligence and Informatics, SACI 2025},
	doi = {10.1109/SACI66288.2025.11030142},
	unique-id = {36157942},
	year = {2025},
	pages = {175-182},
	orcid-numbers = {Ferenci, Tamás/0000-0001-6791-3080; Kovács, Levente/0000-0002-3188-0800}
}

@article{MTMT:35618775,
	title = {Chemotherapy optimization and patient model parameter estimation based on noisy measurements},
	url = {https://m2.mtmt.hu/api/publication/35618775},
	author = {Gergics, Borbála and Puskás, Melánia and Kisbenedek, Lilla and Dömény, Martin Ferenc and Kovács, Levente and Drexler, Dániel András},
	doi = {10.12700/APH.21.10.2024.10.29},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {2},
	unique-id = {35618775},
	issn = {1785-8860},
	abstract = {The application of the achievements of mathematics and informatics greatly helped the development of medicine. Designing personalized therapies using different algorithms is crucial, especially during chemotherapy, to minimize the toxic effects on the patient and avoid resistance, thus ensuring a higher quality of life. In this work, we present an LSTM neural network that can quickly and accurately estimate the parameters of the tumor dynamics model based on noisy virtual patient data. In addition, we present a genetic algorithm designed for therapy optimization, which is able to predict the most appropriate personalized therapy based on the estimated parameters. In this work, we focus on finding the optimal hyperparameters of this genetic algorithm. Optimizing the hyperparameters is of fundamental importance in designing the best possible personalized therapy. © 2024, Budapest Tech Polytechnical Institution. All rights reserved.},
	year = {2024},
	eissn = {1785-8860},
	pages = {475-494},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@article{MTMT:35257810,
	title = {Detecting critical supervision intervals during in silico chemotherapy treatments},
	url = {https://m2.mtmt.hu/api/publication/35257810},
	author = {Dömény, Martin Ferenc and Puskás, Melánia and Kovács, Levente and Mac, Thi Thoa and Drexler, Dániel András},
	doi = {10.12700/APH.21.9.2024.9.17},
	journal-iso = {ACTA POLYTECH HUNG},
	journal = {ACTA POLYTECHNICA HUNGARICA},
	volume = {21},
	unique-id = {35257810},
	issn = {1785-8860},
	year = {2024},
	eissn = {1785-8860},
	pages = {247-261},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:35593021,
	title = {Genetic algorithm-based chemotherapy optimization using basis functions},
	url = {https://m2.mtmt.hu/api/publication/35593021},
	author = {Dömény, Martin Ferenc and Puskás, Melánia and Kovács, Levente and Drexler, Dániel András},
	booktitle = {IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI 2024) : Proceedings},
	doi = {10.1109/CINTI63048.2024.10830914},
	unique-id = {35593021},
	year = {2024},
	pages = {191-196},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:35333016,
	title = {Multi-objective chemotherapy optimization using NSGA-II and Epsilon-constraint method},
	url = {https://m2.mtmt.hu/api/publication/35333016},
	author = {Dömény, Martin Ferenc and Puskás, Melánia and Kovács, Levente and Drexler, Dániel András},
	booktitle = {IEEE 22nd International Symposium on Intelligent Systems and Informatics (SISY 2024)},
	doi = {10.1109/SISY62279.2024.10737606},
	unique-id = {35333016},
	year = {2024},
	pages = {179-184},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@{MTMT:35163678,
	title = {Cyber-Medical Systems in Chemotherapy Treatment Optimization},
	url = {https://m2.mtmt.hu/api/publication/35163678},
	author = {Drexler, Dániel András and Dömény, Martin Ferenc and Ferenci, Tamás and Gergics, Borbála and Kisbenedek, Lilla and Puskás, Melánia and Szűcs, Tamás Dániel and Kovács, Levente},
	booktitle = {Recent Advances in Intelligent Engineering},
	doi = {10.1007/978-3-031-58257-8_13},
	unique-id = {35163678},
	year = {2024},
	pages = {245-269},
	orcid-numbers = {Ferenci, Tamás/0000-0001-6791-3080; Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:34768809,
	title = {Anomaly detection of time series containing tumor volumes},
	url = {https://m2.mtmt.hu/api/publication/34768809},
	author = {Kisbenedek, Lilla and Puskás, Melánia and Kovács, Levente and Drexler, Dániel András},
	booktitle = {IEEE 11th International Conference on Computational Cybernetics and Cyber-Medical Systems, ICCC 2024 : Proceedings},
	doi = {10.1109/ICCC62278.2024.10582921},
	unique-id = {34768809},
	year = {2024},
	pages = {000067-000072},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:34890582,
	title = {Artificial neural networks based cell counting techniques using microscopic images: A review},
	url = {https://m2.mtmt.hu/api/publication/34890582},
	author = {Patakvölgyi, Vivien Roxána and Kovács, Levente and Drexler, Dániel András},
	booktitle = {18th IEEE International Symposium on Applied Computational Intelligence and Informatics SACI 2024 : Proceedings},
	doi = {10.1109/SACI60582.2024.10619063},
	unique-id = {34890582},
	year = {2024},
	pages = {327-331},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:35593185,
	title = {Synthetic data generation based on microscopic images of cancer cells},
	url = {https://m2.mtmt.hu/api/publication/35593185},
	author = {Patakvölgyi, Vivien Roxána and Kovács, Levente and Drexler, Dániel András},
	booktitle = {IEEE 24th International Symposium on Computational Intelligence and Informatics (CINTI 2024) : Proceedings},
	doi = {10.1109/CINTI63048.2024.10830770},
	unique-id = {35593185},
	year = {2024},
	pages = {209-214},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}

@inproceedings{MTMT:35155799,
	title = {Tumor Volume Measurements in Animal Experiments: Current Approaches and Their Limitations},
	url = {https://m2.mtmt.hu/api/publication/35155799},
	author = {Puskás, Melánia and Gergics, Borbála and Kovács, Levente and Drexler, Dániel András},
	booktitle = {System Dependability - Theory and Applications},
	doi = {10.1007/978-3-031-61857-4_20},
	unique-id = {35155799},
	year = {2024},
	pages = {206-217},
	orcid-numbers = {Kovács, Levente/0000-0002-3188-0800}
}