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001-es BibID:	BIBFORM124274
Első szerző:	Alshuwaili, Dhafer Gheni Honi (Informatics)(PhD)
Cím:	A comparative study of pre-trained models in breast ultrasound image segmentation / Dhafer G. Honi, Mohammed Nsaif, Laszlo Szathmary, Szilvia Szeghalmy
Dátum:	2024
Megjegyzések:	In recent years, several deep learning architectures have emerged achieving impressive results in breast ultrasound image segmentation, despite the fact that the problem itself is challenging, because of the variation in lesion size and unequal distribution of intensity in the lesion area. Many of these methods were trained and evaluated on a specific dataset, the Breast Ultrasound Images (BUSI), as it was one of the first publicly available datasets in the field with expert annotations. However, recently, problems with the dataset have been discovered. We conducted our research to estimate, through a few selected methods, the extent to which problems with the dataset make the performance values reported in recent years unreliable. To achieve this, the selected procedures were trained and evaluated along the same methodology on the original and the cleaned datasets. Our results indicate that results related to the BUSI collection should be treated with serious caution.
ISBN:	979-8-3503-8788-9
Tárgyszavak:	Műszaki tudományok Informatikai tudományok konferenciacikk folyóiratcikk segmentation deep learning breast cancer ultrasound images
Megjelenés:	2024 IEEE 3rd Conference on Information Technology and Data Science (CITDS) Proceedings /András Hajdu. - (2024), p. 81-86. -
További szerzők:	Nsaif, Mohammed (informatics) Szathmáry László (1977-) (programtervező-informatikus) Szeghalmy Szilvia (1984-) (programtervező matematikus)
Internet cím:	Intézményi repozitóriumban (DEA) tárolt változat
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