Találati lista | Tudóstér

001-es BibID:	BIBFORM118676
035-os BibID:	(Scopus)85186766577
Első szerző:	Bogacsovics Gergő (informatikus)
Cím:	Assessing Conventional and Deep Learning-Based Approaches for Named Entity Recognition in Unstructured Hungarian Medical Reports / Bogacsovics Gergő, Harangi Balázs, Beregi-Kovács Marcell, Kupás Dávid, Lakatos Róbert, Serbán Norbert Dániel, Tiba Attila, Tóth János
Dátum:	2024
Megjegyzések:	In digital healthcare, much patient data is available in text format. The structuring of this data, according to standards, has yet to be widely used, including in Hungary, where it is available in unstructured form. To make these patient records easy to filter and search, they must be processed and structured. Using modern natural language processing and deep learning techniques has resulted in effective systems for implementing such workflows. However, selecting appropriate algorithms for specific text-processing tasks is still a challenging issue. This is due to the scarcity of benchmarks and the variety of architectures available. This article evaluates models for named entity recognition in digital medical reports written in Hungarian. We evaluate traditional, recurrent neural network, and transformer-based approaches for NER using a dataset comprising 801 PET scans and annotated medical reports. The medical reports were annotated to cover six different entity classes and reviewed by clinical experts to ensure accuracy. We present a comprehensive assessment of various methods and provide insight into addressing NER problems in the case of low-resource languages such as Hungarian.
ISBN:	9798350317206
Tárgyszavak:	Műszaki tudományok Informatikai tudományok előadáskivonat könyvrészlet named entity recognition medical-text record natural language processing deep learning
Megjelenés:	2024 IEEE 22nd World Symposium on Applied Machine Intelligence and Informatics (SAMI) / ed. Kovács Levente, Liberios Vokorokos. - p. 77-82. -
További szerzők:	Harangi Balázs (1986-) (programtervező matematikus) Beregi-Kovács Marcell (1995-) (Alkalmazott matematikus) Kupás Dávid (1996-) (programtervező informatikus) Lakatos Róbert (1986-) (informatikus) Serbán Norbert Dániel (1995-) (informatikus) Tiba Attila (1990-) (informatikus, matematikus) Tóth János (1984-) (programtervező matematikus)
Pályázati támogatás:	2020-1.1.2-PIACI-KFI-2021-00223 Egyéb TKP2021-NKTA-34 Egyéb UNKP-23-3-II-DE-119 Egyéb KDP-2021 C1774095 Egyéb
Internet cím:	Szerző által megadott URL DOI Intézményi repozitóriumban (DEA) tárolt változat
Borító:
Saját polcon:

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
Borító:
Saját polcon: