Zobrazeno 1 - 10
of 483
pro vyhledávání: '"histopathology images"'
Publikováno v:
Scientific Reports, Vol 14, Iss 1, Pp 1-20 (2024)
Abstract Gliomas are primary brain tumors caused by glial cells. These cancers’ classification and grading are crucial for prognosis and treatment planning. Deep learning (DL) can potentially improve the digital pathology investigation of brain tum
Externí odkaz:
https://doaj.org/article/aa951fea49804ce79344f8f8c30a3fb9
Publikováno v:
IEEE Access, Vol 12, Pp 68697-68710 (2024)
Breast cancer is the most commonly diagnosed cancer among women, globally. The occurrence and fatality rates are high for breast cancer compared to other types of cancer. The World Cancer report 2020 points out early detection and rapid treatment as
Externí odkaz:
https://doaj.org/article/13d4e5790d1d4caf9a1b1c308096fdc4
Publikováno v:
Engineering Science and Technology, an International Journal, Vol 51, Iss , Pp 101636- (2024)
In spite of achieving human-level efficacy in gland and nuclei segmentation, modern deep learning-driven techniques still face challenges related to the loss of regional context information and disregard the importance of long-range semantic informat
Externí odkaz:
https://doaj.org/article/42fd19a35ac94e46a1d2f5b12e65b0da
Publikováno v:
Information, Vol 15, Iss 7, p 417 (2024)
Separating overlapped nuclei is a significant challenge in histopathology image analysis. Recently published approaches have achieved promising overall performance on nuclei segmentation; however, their performance on separating overlapped nuclei is
Externí odkaz:
https://doaj.org/article/d8fc420bf26d46b0b46ffb2d671d9d1d
Autor:
Martin Tabakov, Krzysztof Galus, Artur Zawisza, Adam R. Chlopowiec, Adrian B. Chlopowiec, Konrad Karanowski
Publikováno v:
Vietnam Journal of Computer Science, Vol 10, Iss 03, Pp 373-389 (2023)
In this study, we introduce a new synthetic data generation procedure for augmentation of histopathology image data. This is an extension to our previous research in which we proved the possibility to apply deep learning models for morphological anal
Externí odkaz:
https://doaj.org/article/d1f4426295db49669074526bceb06e38
Autor:
Refika Sultan Doğan, Bülent Yılmaz
Publikováno v:
Frontiers in Oncology, Vol 13 (2024)
The field of histopathological image analysis has evolved significantly with the advent of digital pathology, leading to the development of automated models capable of classifying tissues and structures within diverse pathological images. Artificial
Externí odkaz:
https://doaj.org/article/0b9f6ba1eaa04ec59975c437eb678d35
Autor:
Mahesh Anil Inamdar, U. Raghavendra, Anjan Gudigar, Sarvesh Bhandary, Massimo Salvi, Ravinesh C. Deo, Prabal Datta Barua, Edward J. Ciaccio, Filippo Molinari, U. Rajendra Acharya
Publikováno v:
IEEE Access, Vol 11, Pp 108982-108994 (2023)
One of the foremost causes of death in males worldwide is prostate cancer. The identification, detection and diagnosis of the same is very crucial in saving lives. In this paper, we present an efficient gland segmentation model using digital histopat
Externí odkaz:
https://doaj.org/article/db916f7b00a94123938ed171bfc3d4d8
Autor:
Dhayanithi Jaganathan, Sathiyabhama Balasubramaniam, Vidhushavarshini Sureshkumar, Seshathiri Dhanasekaran
Publikováno v:
Diagnostics, Vol 14, Iss 4, p 422 (2024)
Breast cancer remains a significant global public health concern, emphasizing the critical role of accurate histopathological analysis in diagnosis and treatment planning. In recent years, the advent of deep learning techniques has showcased notable
Externí odkaz:
https://doaj.org/article/47edbc2446e44b4980bca7b9993bb035
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.
Akademický článek
Tento výsledek nelze pro nepřihlášené uživatele zobrazit.
K zobrazení výsledku je třeba se přihlásit.
K zobrazení výsledku je třeba se přihlásit.