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pro vyhledávání: '"David Kupas"'
Publikováno v:
Scientific Data, Vol 11, Iss 1, Pp 1-8 (2024)
Abstract Machine learning-based systems have become instrumental in augmenting global efforts to combat cervical cancer. A burgeoning area of research focuses on leveraging artificial intelligence to enhance the cervical screening process, primarily
Externí odkaz:
https://doaj.org/article/e61f5a6e66aa45e288c3d2511cb8ba22
Publikováno v:
Sensors, Vol 24, Iss 9, p 2926 (2024)
Performing a minimally invasive surgery comes with a significant advantage regarding rehabilitating the patient after the operation. But it also causes difficulties, mainly for the surgeon or expert who performs the surgical intervention, since only
Externí odkaz:
https://doaj.org/article/fcffc2ccd89a44c9aa634600ecb9a0d1
Autor:
David Kupas, Balazs Harangi
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2022
The classification of cells extracted from Pap-smears is in most cases done using neural network architectures. Nevertheless, the importance of features extracted with digital image processing is also discussed in many related articles. Decision supp
Autor:
David Kupas, Balazs Harangi
Publikováno v:
Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference. 2021
The low number of annotated training images and class imbalance in the field of machine learning is a common problem that is faced in many applications. With this paper, we focus on a clinical dataset where cells were extracted in a previous research
Publikováno v:
ISPA
Though they rarely become malignant, the surgical removal of fibroids (uterine myomas) is commonly considered to prevent any possible future risks. As the least invasive intervention, endoscopic surgery is the most popular approach for this aim. Howe
Publikováno v:
ISPA
In this paper, we focus on the problem of cell segmentation in digitized Pap smear images, which is a prerequisite of automatically detecting cervical cancer in its early stage. According to the trends, we consider deep learning based approaches in t