@article{MTMT:34226088,
	title = {Author Correction: A biallelic multiple nucleotide length polymorphism explains functional causality at 5p15.33 prostate cancer risk locus (Nature Communications, (2023), 14, 1, (5118), 10.1038/s41467-023-40616-z)},
	url = {https://m2.mtmt.hu/api/publication/34226088},
	author = {Spisak, S. and Tisza, V. and Nuzzo, P.V. and Seo, J.-H. and Pataki, Bálint Ármin and Ribli, Dezső and Sztupinszki, Z. and Bell, C. and Rohanizadegan, M. and Stillman, D.R. and Alaiwi, S.A. and Bartels, A.H. and Papp, M. and Shetty, A. and Abbasi, F. and Lin, X. and Lawrenson, K. and Gayther, S.A. and Pomerantz, M. and Baca, S. and Solymosi, Norbert and Csabai, István and Szallasi, Z. and Gusev, A. and Freedman, M.L.},
	doi = {10.1038/s41467-023-42515-9},
	journal-iso = {NAT COMMUN},
	journal = {NATURE COMMUNICATIONS},
	volume = {14},
	unique-id = {34226088},
	issn = {2041-1723},
	year = {2023},
	eissn = {2041-1723},
	orcid-numbers = {Solymosi, Norbert/0000-0003-1783-2041; Csabai, István/0000-0001-9232-9898}
}

@article{MTMT:34117214,
	title = {A biallelic multiple nucleotide length polymorphism explains functional causality at 5p15.33 prostate cancer risk locus},
	url = {https://m2.mtmt.hu/api/publication/34117214},
	author = {Spisák, Sándor and Tisza, Viktoria and Nuzzo, P.V. and Seo, J.-H. and Pataki, Bálint Ármin and Ribli, Dezső and Sztupinszki, Z. and Bell, C. and Rohanizadegan, M. and Stillman, D.R. and Alaiwi, S.A. and Bartels, A.B. and Papp, Márton and Shetty, A. and Abbasi, F. and Lin, X. and Lawrenson, K. and Gayther, S.A. and Pomerantz, M. and Baca, S. and Solymosi, Norbert and Csabai, István and Szallasi, Z. and Gusev, A. and Freedman, M.L.},
	doi = {10.1038/s41467-023-40616-z},
	journal-iso = {NAT COMMUN},
	journal = {NATURE COMMUNICATIONS},
	volume = {14},
	unique-id = {34117214},
	issn = {2041-1723},
	year = {2023},
	eissn = {2041-1723},
	orcid-numbers = {Papp, Márton/0000-0003-4975-253X; Solymosi, Norbert/0000-0003-1783-2041; Csabai, István/0000-0001-9232-9898}
}

@article{MTMT:32915599,
	title = {HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening},
	url = {https://m2.mtmt.hu/api/publication/32915599},
	author = {Pataki, Bálint Ármin and Olar, Alex and Ribli, Dezső and Pesti, Adrián István and Kontsek, Endre and Gyöngyösi, Benedek Ond and Bilecz, Ágnes and Kovács, Tekla and Kovács, Attila and Kramer, Zsófia and Kiss, András and Szócska, Miklós and Pollner, Péter and Csabai, István},
	doi = {10.1038/s41597-022-01450-y},
	journal-iso = {SCI DATA},
	journal = {SCIENTIFIC DATA},
	volume = {9},
	unique-id = {32915599},
	year = {2022},
	eissn = {2052-4463},
	orcid-numbers = {Olar, Alex/0000-0001-8094-4324; Pesti, Adrián István/0000-0001-6706-6221; Kontsek, Endre/0000-0002-8098-1392; Gyöngyösi, Benedek Ond/0000-0001-5072-3870; Kiss, András/0000-0002-7453-3163; Szócska, Miklós/0000-0003-0648-9778; Pollner, Péter/0000-0003-0464-4893; Csabai, István/0000-0001-9232-9898}
}

@mastersthesis{MTMT:32774543,
	title = {Mesterséges neurális hálózatok alkalmazása és elemzése adatintenzív tudományos problémákban},
	url = {https://m2.mtmt.hu/api/publication/32774543},
	author = {Ribli, Dezső},
	unique-id = {32774543},
	year = {2021}
}

@article{MTMT:31208336,
	title = {Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms},
	url = {https://m2.mtmt.hu/api/publication/31208336},
	author = {Schaffter, Thomas and Buist, Diana S. M. and Lee, Christoph I. and Nikulin, Yaroslav and Ribli, Dezső and Guan, Yuanfang and Lotter, William and Jie, Zequn and Du, Hao and Wang, Sijia and Feng, Jiashi and Feng, Mengling and Kim, Hyo-Eun and Albiol, Francisco and Albiol, Alberto and Morrell, Stephen and Wojna, Zbigniew and Ahsen, Mehmet Eren and Asif, Umar and Jimeno Yepes, Antonio and Yohanandan, Shivanthan and Rabinovici-Cohen, Simona and Yi, Darvin and Hoff, Bruce and Yu, Thomas and Chaibub Neto, Elias and Rubin, Daniel L. and Lindholm, Peter and Margolies, Laurie R. and McBride, Russell Bailey and Rothstein, Joseph H. and Sieh, Weiva and Ben-Ari, Rami and Harrer, Stefan and Trister, Andrew and Friend, Stephen and Norman, Thea and Sahiner, Berkman and Strand, Fredrik and Guinney, Justin and Stolovitzky, Gustavo and Mackey, Lester and Cahoon, Joyce and Shen, Li and Sohn, Jae Ho and Trivedi, Hari and Shen, Yiqiu and Buturovic, Ljubomir and Pereira, Jose Costa and Cardoso, Jaime S. and Castro, Eduardo and Kalleberg, Karl Trygve and Pelka, Obioma and Nedjar, Imane and Geras, Krzysztof J. and Nensa, Felix and Goan, Ethan and Koitka, Sven and Caballero, Luis and Cox, David D. and Krishnaswamy, Pavitra and Pandey, Gaurav and Friedrich, Christoph M. and Perrin, Dimitri and Fookes, Clinton and Shi, Bibo and Cardoso Negrie, Gerard and Kawczynski, Michael and Cho, Kyunghyun and Khoo, Can Son and Lo, Joseph Y. and Sorensen, A. Gregory and Jung, Hwejin},
	doi = {10.1001/jamanetworkopen.2020.0265},
	journal-iso = {JAMA NETW OPEN},
	journal = {JAMA NETWORK OPEN},
	volume = {3},
	unique-id = {31208336},
	year = {2020},
	eissn = {2574-3805},
	pages = {e200265}
}

@article{MTMT:30806082,
	title = {Weak lensing cosmology with convolutional neural networks on noisy data},
	url = {https://m2.mtmt.hu/api/publication/30806082},
	author = {Ribli, Dezső and Pataki, Bálint Ármin and José, Manuel Zorrilla Matilla and Daniel, Hsu and Zoltán, Haiman and Csabai, István},
	doi = {10.1093/mnras/stz2610},
	journal-iso = {MON NOT R ASTRON SOC},
	journal = {MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY},
	volume = {490},
	unique-id = {30806082},
	issn = {0035-8711},
	year = {2019},
	eissn = {1365-2966},
	pages = {1843-1860},
	orcid-numbers = {Csabai, István/0000-0001-9232-9898}
}

@article{MTMT:30620379,
	title = {Galaxy shape measurement with convolutional neural networks},
	url = {https://m2.mtmt.hu/api/publication/30620379},
	author = {Ribli, Dezső and Dobos, László and Csabai, István},
	doi = {10.1093/mnras/stz2374},
	journal-iso = {MON NOT R ASTRON SOC},
	journal = {MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY},
	volume = {489},
	unique-id = {30620379},
	issn = {0035-8711},
	year = {2019},
	eissn = {1365-2966},
	pages = {4847-4859},
	orcid-numbers = {Dobos, László/0000-0001-7679-9478; Csabai, István/0000-0001-9232-9898}
}

@article{MTMT:30445946,
	title = {A számítógépes mélytanulási technológia várható megjelenése a hazai mammográfiában},
	url = {https://m2.mtmt.hu/api/publication/30445946},
	author = {Ribli, Dezső and Zsuppán, Richárd and Pollner, Péter and Horváth, Anna and Bánsághi, Zoltán and Csabai, István and Bérczi, Viktor and Unger, Zsuzsa},
	doi = {10.1556/650.2019.31263},
	journal-iso = {ORV HETIL},
	journal = {ORVOSI HETILAP},
	volume = {160},
	unique-id = {30445946},
	issn = {0030-6002},
	abstract = {Introduction and aim: The technology, named 'deep learning' is the promising result of the last two decades of development in computer science. It poses an unavoidable challenge for medicine, how to understand, apply and adopt the - today not fully explored - possibilities that have become available by these new methods. Method: It is a gift and a mission, since the exponentially growing volume of raw data (from imaging, laboratory, therapy diagnostics or therapy interactions, etc.) did not solve until now our wished and aimed goal to treat patients according to their personal status and setting or specific to their tumor and disease. Results: Currently, as a responsible health care provider and financier, we face the problem of supporting suboptimal procedures and protocols either at individual or at community level. The problem roots in the overwhelming amount of data and, at the same time, the lack of targeted information for treatment. We expect from the deep learning technology an aid which helps to reinforce and extend the human-human cooperations in patient-doctor visits. We expect that computers take over the tedious work allowing to revive the core of healing medicine: the insightful meeting and discussion between patients and medical experts. Conclusion: We should learn the revelational possibilities of deep learning techniques that can help to overcome our recognized finite capacities in data processing and integration. If we, doctors and health care providers or decision makers, are able to abandon our fears and prejudices, then we can utilize this new tool not only in imaging diagnostics but also for daily therapies (eg., immune therapy). The paper aims to make a great mind to do this.},
	keywords = {Artificial intelligence; machine learning; Mammography; User-Computer Interface; Mesterséges intelligencia; gépi tanulás; mammográfia; számítógép és felhasználó együttműködése},
	year = {2019},
	eissn = {1788-6120},
	pages = {138-143},
	orcid-numbers = {Pollner, Péter/0000-0003-0464-4893; Horváth, Anna/0000-0003-3229-5643; Csabai, István/0000-0001-9232-9898; Bérczi, Viktor/0000-0003-4386-2527}
}

@article{MTMT:30417572,
	title = {The genomic imprint of cancer therapies helps timing the formation of metastases},
	url = {https://m2.mtmt.hu/api/publication/30417572},
	author = {Németh, Eszter and Krzystanek, Marcin and Reiniger, Lilla and Ribli, Dezső and Pipek, Orsolya Anna and Sztupinszki, Zsófia and Glasz, Tibor and Csabai, István and Moldvay, Judit and Szállási, Zoltán and Szüts, Dávid},
	doi = {10.1002/ijc.32159},
	journal-iso = {INT J CANCER},
	journal = {INTERNATIONAL JOURNAL OF CANCER},
	volume = {145},
	unique-id = {30417572},
	issn = {0020-7136},
	year = {2019},
	eissn = {1097-0215},
	pages = {694-704},
	orcid-numbers = {Reiniger, Lilla/0000-0003-2248-4264; Pipek, Orsolya Anna/0000-0001-8109-0340; Glasz, Tibor/0000-0003-2947-2733; Csabai, István/0000-0001-9232-9898; Szállási, Zoltán/0000-0001-5395-7509; Szüts, Dávid/0000-0001-7985-0136}
}

@article{MTMT:30403364,
	title = {An improved cosmological parameter inference scheme motivated by deep learning},
	url = {https://m2.mtmt.hu/api/publication/30403364},
	author = {Ribli, Dezső and Pataki, Bálint Ármin and Csabai, István},
	doi = {10.1038/s41550-018-0596-8},
	journal-iso = {NAT ASTRON},
	journal = {NATURE ASTRONOMY},
	volume = {3},
	unique-id = {30403364},
	issn = {2397-3366},
	abstract = {Dark matter cannot be observed directly, but its weak gravitational lensing slightly distorts the apparent shapes of background galaxies, making weak lensing one of the most promising probes of cosmology. Several observational studies have measured the effect, and there are currently running(1,2) and planned efforts(3,4) to provide even larger and higher-resolution weak lensing maps. Owing to nonlinearities on small scales, the traditional analysis with two-point statistics does not fully capture all of the underlying informations(5). Multiple inference methods have been proposed to extract more details based on higher-order statistics(6,7), peak statisticss(8-13), Minkowski functionals(14-16) and recently convolutional neural networks(17,18). Here we present an improved convolutional neural network that gives significantly better estimates of the Omega(m) and sigma(8) cosmological parameters from simulated weak lensing convergence maps than state-of-art methods and that is also free of systematic bias. We show that the network exploits information in the gradients around peaks, and with this insight we have constructed an easy-to-understand and robust peak-counting algorithm based on the steepness of peaks, instead of their heights. The proposed scheme is even more accurate than the neural network on high-resolution noiseless maps. With shape noise and lower resolution, its relative advantage deteriorates, but it remains more accurate than peak counting.},
	keywords = {WEAK LENSING MEASUREMENTS; PEAK STATISTICS},
	year = {2019},
	eissn = {2397-3366},
	pages = {93-98},
	orcid-numbers = {Csabai, István/0000-0001-9232-9898}
}