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001-es BibID:	BIBFORM102065
Első szerző:	Harangi Balázs (programtervező matematikus)
Cím:	Cell detection on digitized Pap smear images using ensemble of conventional image processing and deep learning techniques / Harangi Balázs, Tóth János, Bogacsovics Gergő, Kupás Dávid, Kovács László, Hajdu András
Dátum:	2019
Megjegyzések:	In this paper, we focus on the problem of cell segmentation in digitized Pap smear images, which is a pre-requisite of automatically detecting cervical cancer in its early stage. According to the trends, we consider deep learning based approaches in the form of applying fully convolutional neural networks (FCNNs). A common bottleneck of deep learning is that large annotated dataset is required for proper training. As large public datasets are not yet available in this field, we have composed a corresponding manually labeled dataset. Though this dataset is quite large, the manual annotation is less reliable in this domain, so we had to apply such a deep learning framework that is able to overcome this issue. Accordingly, we have applied such an ensemble of FCNN and traditional segmentation approaches that provide sufficiently large diversity according to the most challenging manual annotation-related issues, like the inaccurate selection of cell boundaries. We propose ensembles to merge the outputs of the different segmentation methods, which have been proven superior to any of the ensemble members according to our experimental studies.
ISBN:	9781728131405
Tárgyszavak:	Műszaki tudományok Informatikai tudományok előadáskivonat könyvrészlet Pap smear test cell segmentation deep learning region-based combination
Megjelenés:	11th International Symposium on Image and Signal Processing and Analysis (ISPA 2019) / eds. S. Lončarić, R. Bregović, M. Carli, M. Subašić. - p. 38-42. -
További szerzők:	Tóth János (1984-) (programtervező matematikus) Bogacsovics Gergő (1996-) (informatikus) Kupás Dávid (1996-) (programtervező informatikus) Kovács László (1984-) (informatikus) Hajdu András (1973-) (matematikus, informatikus)
Pályázati támogatás:	Bolyai János Kutatási Ösztöndíj MTA EFOP-3.6.2-16-2017-00015 EFOP GINOP-2.2.1-18-2018-00012 GINOP
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
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