TY - JOUR AU - Spisak, S. AU - Tisza, V. AU - Nuzzo, P.V. AU - Seo, J.-H. AU - Pataki, Bálint Ármin AU - Ribli, Dezső AU - Sztupinszki, Z. AU - Bell, C. AU - Rohanizadegan, M. AU - Stillman, D.R. AU - Alaiwi, S.A. AU - Bartels, A.H. AU - Papp, M. AU - Shetty, A. AU - Abbasi, F. AU - Lin, X. AU - Lawrenson, K. AU - Gayther, S.A. AU - Pomerantz, M. AU - Baca, S. AU - Solymosi, Norbert AU - Csabai, István AU - Szallasi, Z. AU - Gusev, A. AU - Freedman, M.L. TI - 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) JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 14 PY - 2023 IS - 1 SN - 2041-1723 DO - 10.1038/s41467-023-42515-9 UR - https://m2.mtmt.hu/api/publication/34226088 ID - 34226088 N1 - Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, 02215, MA, United States Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, 02215, MA, United States Computational Health Informatics Program (CHIP) Boston Children’s Hospital Harvard Medical School, Boston, 02215, MA, United States Institute of Enzymology, Research Centre for Natural Sciences, Budapest, 1117, Hungary Department of Internal Medicine, School of Medicine, University of Genoa, Lgo R. Benzi 10, Genoa, 16132, Italy Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Pázmány P. s. 1A, Budapest, 1117, Hungary Centre for Bioinformatics, University of Veterinary Medicine, Istvan str. 2, Budapest, 1078, Hungary Division of Genetics, Brigham & Women’s Hospital, Boston, MA, United States Women’s Cancer Program, Samuel Oschin Comprehensive Cancer Institute, Cedars-Sinai Medical Center, Los Angeles, 90048, CA, United States Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Cedars-Sinai Medical Center, Los Angeles, 90048, CA, United States Center for Bioinformatics and Functional Genomics, Department of Biomedical Science, Cedars-Sinai Medical Center, Los Angeles, 90048, CA, United States The Eli and Edythe L. Broad Institute, Cambridge, 02142, MA, United States Department of Bioinformatics, Forensic and Insurance Medicine Semmelweis University, Budapest, Hungary Danish Cancer Society Research Center, Strandboulevarden 49, Copenhagen, 2100, Denmark National Korányi Institute of Pulmonology, Budapest, 1112, Hungary Export Date: 30 October 2023; Cited By: 0; Correspondence Address: M.L. Freedman; Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, 02215, United States; email: matthew_freedman@dfci.harvard.edu LA - English DB - MTMT ER - TY - JOUR AU - Spisák, Sándor AU - Tisza, Viktoria AU - Nuzzo, P.V. AU - Seo, J.-H. AU - Pataki, Bálint Ármin AU - Ribli, Dezső AU - Sztupinszki, Z. AU - Bell, C. AU - Rohanizadegan, M. AU - Stillman, D.R. AU - Alaiwi, S.A. AU - Bartels, A.B. AU - Papp, Márton AU - Shetty, A. AU - Abbasi, F. AU - Lin, X. AU - Lawrenson, K. AU - Gayther, S.A. AU - Pomerantz, M. AU - Baca, S. AU - Solymosi, Norbert AU - Csabai, István AU - Szallasi, Z. AU - Gusev, A. AU - Freedman, M.L. TI - A biallelic multiple nucleotide length polymorphism explains functional causality at 5p15.33 prostate cancer risk locus JF - NATURE COMMUNICATIONS J2 - NAT COMMUN VL - 14 PY - 2023 IS - 1 SN - 2041-1723 DO - 10.1038/s41467-023-40616-z UR - https://m2.mtmt.hu/api/publication/34117214 ID - 34117214 LA - English DB - MTMT ER - TY - JOUR AU - Pataki, Bálint Ármin AU - Olar, Alex AU - Ribli, Dezső AU - Pesti, Adrián István AU - Kontsek, Endre AU - Gyöngyösi, Benedek Ond AU - Bilecz, Ágnes AU - Kovács, Tekla AU - Kovács, Attila AU - Kramer, Zsófia AU - Kiss, András AU - Szócska, Miklós AU - Pollner, Péter AU - Csabai, István TI - HunCRC: annotated pathological slides to enhance deep learning applications in colorectal cancer screening JF - SCIENTIFIC DATA J2 - SCI DATA VL - 9 PY - 2022 IS - 1 PG - 7 SN - 2052-4463 DO - 10.1038/s41597-022-01450-y UR - https://m2.mtmt.hu/api/publication/32915599 ID - 32915599 LA - English DB - MTMT ER - TY - THES AU - Ribli, Dezső TI - Mesterséges neurális hálózatok alkalmazása és elemzése adatintenzív tudományos problémákban PY - 2021 SP - 143 UR - https://m2.mtmt.hu/api/publication/32774543 ID - 32774543 LA - Hungarian DB - MTMT ER - TY - JOUR AU - Schaffter, Thomas AU - Buist, Diana S. M. AU - Lee, Christoph I. AU - Nikulin, Yaroslav AU - Ribli, Dezső AU - Guan, Yuanfang AU - Lotter, William AU - Jie, Zequn AU - Du, Hao AU - Wang, Sijia AU - Feng, Jiashi AU - Feng, Mengling AU - Kim, Hyo-Eun AU - Albiol, Francisco AU - Albiol, Alberto AU - Morrell, Stephen AU - Wojna, Zbigniew AU - Ahsen, Mehmet Eren AU - Asif, Umar AU - Jimeno Yepes, Antonio AU - Yohanandan, Shivanthan AU - Rabinovici-Cohen, Simona AU - Yi, Darvin AU - Hoff, Bruce AU - Yu, Thomas AU - Chaibub Neto, Elias AU - Rubin, Daniel L. AU - Lindholm, Peter AU - Margolies, Laurie R. AU - McBride, Russell Bailey AU - Rothstein, Joseph H. AU - Sieh, Weiva AU - Ben-Ari, Rami AU - Harrer, Stefan AU - Trister, Andrew AU - Friend, Stephen AU - Norman, Thea AU - Sahiner, Berkman AU - Strand, Fredrik AU - Guinney, Justin AU - Stolovitzky, Gustavo AU - Mackey, Lester AU - Cahoon, Joyce AU - Shen, Li AU - Sohn, Jae Ho AU - Trivedi, Hari AU - Shen, Yiqiu AU - Buturovic, Ljubomir AU - Pereira, Jose Costa AU - Cardoso, Jaime S. AU - Castro, Eduardo AU - Kalleberg, Karl Trygve AU - Pelka, Obioma AU - Nedjar, Imane AU - Geras, Krzysztof J. AU - Nensa, Felix AU - Goan, Ethan AU - Koitka, Sven AU - Caballero, Luis AU - Cox, David D. AU - Krishnaswamy, Pavitra AU - Pandey, Gaurav AU - Friedrich, Christoph M. AU - Perrin, Dimitri AU - Fookes, Clinton AU - Shi, Bibo AU - Cardoso Negrie, Gerard AU - Kawczynski, Michael AU - Cho, Kyunghyun AU - Khoo, Can Son AU - Lo, Joseph Y. AU - Sorensen, A. Gregory AU - Jung, Hwejin TI - Evaluation of Combined Artificial Intelligence and Radiologist Assessment to Interpret Screening Mammograms JF - JAMA NETWORK OPEN J2 - JAMA NETW OPEN VL - 3 PY - 2020 IS - 3 SP - e200265 SN - 2574-3805 DO - 10.1001/jamanetworkopen.2020.0265 UR - https://m2.mtmt.hu/api/publication/31208336 ID - 31208336 N1 - Computational Oncology, Sage Bionetworks, Seattle, WA, United States Kaiser Permanente Washington Health Research Institute, Seattle, WA, United States University of Washington School of Medicine, Seattle, United States Therapixel, Paris, France Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Budapest, Hungary Department of Computational Medicine and Bioinformatics, Michigan Medicine, University of Michigan, Ann Arbor, United States DeepHealth Inc., Cambridge, MA, United States Tencent AI Lab, Shenzhen, China National University of Singapore, Singapore, Singapore Integrated Health Information Systems Pte Ltd., Singapore, Singapore Department of Electrical and Computer Engineering, National University of Singapore, Singapore, Singapore National University Health System, Singapore, Singapore Lunit Inc., Seoul, South Korea Instituto de Física Corpuscular Paterna, Comunitat Valenciana, Spain Universitat Politecnica de Valencia, Valenciana, Valencia, Spain Centre for Medical Image Computing, University College London, Bloomsbury, London, United Kingdom Tensorflight Inc., Mountain View, CA, United States University of Illinois at Urbana-Champaign, Urbana, United States IBM Research Australia, Melbourne, Australia IBM Research Haifa, Haifa University Campus, Mount Carmel, Haifa, Israel Stanford University, Stanford, CA, United States Department of Biomedical Data Science, Radiology, and Medicine (Biomedical Informatics), Stanford University, Stanford, CA, United States Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden Department of Diagnostic, Molecular and Interventional Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, United States Department of Pathology, Molecular and Cell-Based Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, United States Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States Department of Population Health Science and Policy, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, United States Fred Hutchinson Cancer Research Center, Seattle, WA, United States Bill and Melinda Gates Foundation, Seattle, WA, United States Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, MD, United States Department of Oncology-Pathology, Karolinska Institutet, Stockholm, Sweden Breast Radiology, Karolinska University Hospital, Stockholm, Sweden IBM Research, Translational Systems Biology and Nanobiotechnology, Thomas J. Watson Research Center, Yorktown Heights, NY, United States Microsoft New England Research and Development Center, Cambridge, MA, United States North Carolina State University, Raleigh, United States Icahn School of Medicine at Mount Sinai, New York, NY, United States Department of Radiology and Biomedical Imaging, University of California, San Francisco, San Francisco, United States Emory University, Atlanta, GA, United States New York University, New York, United States Clinical Persona, East Palo Alto, CA, United States Institute for Systems and Computer Engineering, Technology and Science, Porto, Portugal KolibriFX, Oslo, Norway Department of Computer Science, University of Applied Sciences and Arts, Dortmund, Germany Department of Diagnostic and Interventional Radiology and Neuroradiology, University Hospital Essen, Essen, Germany Biomedical Engineering Laboratory, Tlemcen University, Tlemcen, Algeria Department of Radiology, NYU School of Medicine, New York, NY, United States Queensland University of Technology, Brisbane, Australia MIT-IBMWatson AI Lab, IBM Research, Cambridge, MA, United States Institute for Infocomm Research, A∗STAR, Singapore, Singapore Icahn Institute for Data Science and Genomic Technology, New York, NY, United States Carl E. Ravin Advanced Imaging Laboratories, Department of Radiology, Duke University School of Medicine, Durham, NC, United States Satalia, London, United Kingdom Bakar Computational Health Sciences Institute, University of California, San Francisco, San Francisco, United States University College London, London, United Kingdom Department of Radiology, Duke University School of Medicine, Durham, NC, United States Korea University, Seoul, South Korea Cited By :217 Export Date: 11 April 2024 Correspondence Address: Stolovitzky, G.; IBM Translational Systems Biology and Nanobiotechnology Program, 1101 Kitchawan Rd, United States; email: gustavo@us.ibm.com LA - English DB - MTMT ER - TY - JOUR AU - Ribli, Dezső AU - Pataki, Bálint Ármin AU - José, Manuel Zorrilla Matilla AU - Daniel, Hsu AU - Zoltán, Haiman AU - Csabai, István TI - Weak lensing cosmology with convolutional neural networks on noisy data JF - MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY J2 - MON NOT R ASTRON SOC VL - 490 PY - 2019 IS - 2 SP - 1843 EP - 1860 PG - 18 SN - 0035-8711 DO - 10.1093/mnras/stz2610 UR - https://m2.mtmt.hu/api/publication/30806082 ID - 30806082 N1 - Department of Physics of Complex Systems, ELTE Eötvös Loránd University, Pf. 32 H-1518, Budapest, Hungary Department of Astronomy, Columbia University, New York, NY 10027, United States Department of Computer Science, Columbia University, New York, NY 10027, United States Cited By :48 Export Date: 22 March 2024 CODEN: MNRAA LA - English DB - MTMT ER - TY - JOUR AU - Ribli, Dezső AU - Dobos, László AU - Csabai, István TI - Galaxy shape measurement with convolutional neural networks JF - MONTHLY NOTICES OF THE ROYAL ASTRONOMICAL SOCIETY J2 - MON NOT R ASTRON SOC VL - 489 PY - 2019 IS - 4 SP - 4847 EP - 4859 PG - 13 SN - 0035-8711 DO - 10.1093/mnras/stz2374 UR - https://m2.mtmt.hu/api/publication/30620379 ID - 30620379 N1 - Cited By :5 Export Date: 22 March 2024 CODEN: MNRAA Correspondence Address: Ribli, D.; Department of Physics of Complex Systems, Pf. 32 H-1518, Hungary; email: dkrib@caesar.elte.hu LA - English DB - MTMT ER - TY - JOUR AU - Ribli, Dezső AU - Zsuppán, Richárd AU - Pollner, Péter AU - Horváth, Anna AU - Bánsághi, Zoltán AU - Csabai, István AU - Bérczi, Viktor AU - Unger, Zsuzsa TI - A számítógépes mélytanulási technológia várható megjelenése a hazai mammográfiában JF - ORVOSI HETILAP J2 - ORV HETIL VL - 160 PY - 2019 IS - 4 SP - 138 EP - 143 PG - 6 SN - 0030-6002 DO - 10.1556/650.2019.31263 UR - https://m2.mtmt.hu/api/publication/30445946 ID - 30445946 N1 - Eötvös Loránd Tudományegyetem, Komplex Rendszerek Fizikája Tanszék, Budapest, Hungary Semmelweis Egyetem, Általános Orvostudományi Kar, Radiológiai Klinika, Budapest, Hungary MTA-ELTE Statisztikus és Biológiai Fizika Kutatócsoport, Pázmány P. sétány 1/A, Budapest, 1117, Hungary Semmelweis Egyetem, Általános Orvostudományi Kar, III, Belgyógyászati Klinika, Budapest, Hungary Cited By :1 Export Date: 29 February 2024 CODEN: ORHEA Correspondence Address: Pollner, P.; MTA-ELTE Statisztikus és Biológiai Fizika Kutatócsoport, Pázmány P. sétány 1/A, Hungary; email: pollner@angel.elte.hu AB - 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. LA - Hungarian DB - MTMT ER - TY - JOUR AU - Németh, Eszter AU - Krzystanek, Marcin AU - Reiniger, Lilla AU - Ribli, Dezső AU - Pipek, Orsolya Anna AU - Sztupinszki, Zsófia AU - Glasz, Tibor AU - Csabai, István AU - Moldvay, Judit AU - Szállási, Zoltán AU - Szüts, Dávid TI - The genomic imprint of cancer therapies helps timing the formation of metastases JF - INTERNATIONAL JOURNAL OF CANCER J2 - INT J CANCER VL - 145 PY - 2019 IS - 3 SP - 694 EP - 704 PG - 11 SN - 0020-7136 DO - 10.1002/ijc.32159 UR - https://m2.mtmt.hu/api/publication/30417572 ID - 30417572 LA - English DB - MTMT ER - TY - JOUR AU - Ribli, Dezső AU - Pataki, Bálint Ármin AU - Csabai, István TI - An improved cosmological parameter inference scheme motivated by deep learning JF - NATURE ASTRONOMY J2 - NAT ASTRON VL - 3 PY - 2019 IS - 1 SP - 93 EP - 98 PG - 6 SN - 2397-3366 DO - 10.1038/s41550-018-0596-8 UR - https://m2.mtmt.hu/api/publication/30403364 ID - 30403364 AB - 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. LA - English DB - MTMT ER -