Zobrazeno 1 - 10
of 47
pro vyhledávání: '"Mazen, Sudah"'
Autor:
Hidemi Okuma, Amro Masarwah, Aleksandr Istomin, Aki Nykänen, Juhana Hakumäki, Ritva Vanninen, Mazen Sudah
Publikováno v:
European Journal of Radiology Open, Vol 13, Iss , Pp 100611- (2024)
Objectives: To examine the background parenchymal enhancement (BPE) levels in peri-menopausal breast MRI compared with pre- and post-menopausal breast MRI. Methods: This study included 562 patients (55.8±12.3 years) who underwent contrast-enhanced d
Externí odkaz:
https://doaj.org/article/18514d0d528a42429cfc556ab9f11211
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-14 (2024)
Abstract Interval breast cancers are diagnosed between scheduled screenings and differ in many respects from screening-detected cancers. Studies comparing the survival of patients with interval and screening-detected cancers have reported differing r
Externí odkaz:
https://doaj.org/article/0ece8c3546164025829bb3bbb4ae760e
Autor:
Naga Raju Gudhe, Hamid Behravan, Mazen Sudah, Hidemi Okuma, Ritva Vanninen, Veli-Matti Kosma, Arto Mannermaa
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-19 (2022)
Abstract Breast density, which is a measure of the relative amount of fibroglandular tissue within the breast area, is one of the most important breast cancer risk factors. Accurate segmentation of fibroglandular tissues and breast area is crucial fo
Externí odkaz:
https://doaj.org/article/8bab9eed745d4985ae4586b80da7e185
Autor:
Naga Raju Gudhe, Hamid Behravan, Mazen Sudah, Hidemi Okuma, Ritva Vanninen, Veli-Matti Kosma, Arto Mannermaa
Publikováno v:
Scientific Reports, Vol 11, Iss 1, Pp 1-18 (2021)
Abstract We propose a novel multi-level dilated residual neural network, an extension of the classical U-Net architecture, for biomedical image segmentation. U-Net is the most popular deep neural architecture for biomedical image segmentation, howeve
Externí odkaz:
https://doaj.org/article/85e9a86e1d424523bea41de106b48ead
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Autor:
Hidemi Okuma, Mazen Sudah, Tiia Kettunen, Anton Niukkanen, Anna Sutela, Amro Masarwah, Veli-Matti Kosma, Päivi Auvinen, Arto Mannermaa, Ritva Vanninen
Publikováno v:
PLoS ONE, Vol 15, Iss 6, p e0235278 (2020)
PurposeThe apparent diffusion coefficient (ADC) is increasingly used to characterize breast cancer. The peritumor/tumor ADC ratio is suggested to be a reliable and generally applicable index. However, its overall prognostication value remains unclear
Externí odkaz:
https://doaj.org/article/82fd656d4144432da6f0bb6fcc2800d0
Publikováno v:
Medical Imaging 2023: Digital and Computational Pathology.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::dde32f8fdc9d09b9dc5614aaf84afd1a
https://doi.org/10.1117/12.2654117
https://doi.org/10.1117/12.2654117
Publikováno v:
Annals of Surgical Oncology. 29:6716-6727
Background Multifocal or complex breast lesions are a challenge for breast-conserving surgery, particularly surgery in small breasts or those located in the upper inner quadrant. The dual-layer rotation technique exploits the idea of manipulating the
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::2e9ef9472a83b5318dd5ebffc35e789a
https://doi.org/10.1245/s10434-022-11977-4
https://doi.org/10.1245/s10434-022-11977-4
Autor:
Aleksandr Istomin, Amro Masarwah, Marja Pitkänen, Sarianna Joukainen, Anna Sutela, Ritva Vanninen, Mazen Sudah
Publikováno v:
PLoS ONE, Vol 13, Iss 10, p e0204326 (2018)
PURPOSE:To evaluate the malignancy rate and diagnostic performance of galactography in patients with pathological nipple discharge (PND) after negative clinical breast examination, mammography and ultrasound. MATERIALS AND METHODS:We retrospectively
Externí odkaz:
https://doaj.org/article/522777713afb46f4adcaba39c47198b4
Publikováno v:
Annals of surgical oncology. 29(11)
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::ca728479b19efa0aabea0e839c31f6ed
https://pubmed.ncbi.nlm.nih.gov/35816245
https://pubmed.ncbi.nlm.nih.gov/35816245