Zobrazeno 1 - 10
of 2 272
pro vyhledávání: '"Multiple-instance learning"'
Publikováno v:
Journal of Pathology Informatics, Vol 15, Iss , Pp 100403- (2024)
Whole slide images (WSI), obtained by high-resolution digital scanning of microscope slides at multiple scales, are the cornerstone of modern Digital Pathology. However, they represent a particular challenge to artificial intelligence (AI)-based/AI-m
Externí odkaz:
https://doaj.org/article/ffab4fd4cef8405ea99bd9b5d4dfe1f6
Autor:
Noriaki Hashimoto, Hiroyuki Hanada, Hiroaki Miyoshi, Miharu Nagaishi, Kensaku Sato, Hidekata Hontani, Koichi Ohshima, Ichiro Takeuchi
Publikováno v:
Journal of Pathology Informatics, Vol 15, Iss , Pp 100359- (2024)
In this study, we present a deep-learning-based multimodal classification method for lymphoma diagnosis in digital pathology, which utilizes a whole slide image (WSI) as the primary image data and flow cytometry (FCM) data as auxiliary information. I
Externí odkaz:
https://doaj.org/article/9d5f8550a204454c965758cbe4462f42
Autor:
Qingyuan Zheng, Xinyu Wang, Rui Yang, Junjie Fan, Jingping Yuan, Xiuheng Liu, Lei Wang, Zhuoni Xiao, Zhiyuan Chen
Publikováno v:
Cancer Medicine, Vol 13, Iss 16, Pp n/a-n/a (2024)
Abstract Background Tumor mutation burden (TMB) and VHL mutation play a crucial role in the management of patients with clear cell renal cell carcinoma (ccRCC), such as guiding adjuvant chemotherapy and improving clinical outcomes. However, the time
Externí odkaz:
https://doaj.org/article/958a9f80ac0d44b48c3f379119d3a381
Autor:
Rukhma Aftab, Qiang Yan, Juanjuan Zhao, Gao Yong, Yue Huajie, Zia Urrehman, Faizi Mohammad Khalid
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
IntroductionPathologists rely on whole slide images (WSIs) to diagnose cancer by identifying tumor cells and subtypes. Deep learning models, particularly weakly supervised ones, classify WSIs using image tiles but may overlook false positives and neg
Externí odkaz:
https://doaj.org/article/3a0f00d608504b21a5e51bd6d9d1b891
Publikováno v:
IEEE Access, Vol 12, Pp 78409-78422 (2024)
This article presents a systematic review of Multiple Instance Learning (MIL) applied to image classification, specifically highlighting its applications in medical imaging. Motivated by the need for a comprehensive and up-to-date analysis due to the
Externí odkaz:
https://doaj.org/article/3162c829c5ef4988bc9387ba72b3843a
Publikováno v:
IEEE Access, Vol 12, Pp 6768-6776 (2024)
Diabetic retinopathy (DR) is an irreversible fundus retinopathy. A deep learning-based automated DR diagnosis system can save diagnostic time. While Transformer has shown superior performance compared to Convolutional Neural Network (CNN), it typical
Externí odkaz:
https://doaj.org/article/fc944e2a936042b49dc541367c48fb86
Autor:
Thomas E. Tavolara, M. Khalid Khan Niazi, Andrew L. Feldman, David L. Jaye, Christopher Flowers, Lee A.D. Cooper, Metin N. Gurcan
Publikováno v:
Diagnostic Pathology, Vol 19, Iss 1, Pp 1-13 (2024)
Abstract Background c-MYC and BCL2 positivity are important prognostic factors for diffuse large B-cell lymphoma. However, manual quantification is subject to significant intra- and inter-observer variability. We developed an automated method for qua
Externí odkaz:
https://doaj.org/article/7c68eb8f1a58429a94ccbcc88f6fa070
Publikováno v:
IEEE Access, Vol 12, Pp 1374-1385 (2024)
Pulmonary tuberculosis (PTB) is a major global health threat. Diagnosing PTB infectiousness is vital for clinical decision-making, but existing etiological examination methods do not meet the requirements for speed, accuracy, and cost effectiveness.
Externí odkaz:
https://doaj.org/article/cdb825b3d9914044830fec3436b09b79
Autor:
Qinqing Wang, Qiu Bi, Linhao Qu, Yuchen Deng, Xianhong Wang, Yijun Zheng, Chenrong Li, Qingyin Meng, Kun Miao
Publikováno v:
Frontiers in Oncology, Vol 14 (2024)
BackgroundWhole Slide Image (WSI) analysis, driven by deep learning algorithms, has the potential to revolutionize tumor detection, classification, and treatment response prediction. However, challenges persist, such as limited model generalizability
Externí odkaz:
https://doaj.org/article/a1b15e5fa1ca4f8f977293802b64bf66
Autor:
Stepan Romanov, Sacha Howell, Elaine Harkness, Megan Bydder, D. Gareth Evans, Steven Squires, Martin Fergie, Sue Astley
Publikováno v:
Tomography, Vol 9, Iss 6, Pp 2103-2115 (2023)
Accurate prediction of individual breast cancer risk paves the way for personalised prevention and early detection. The incorporation of genetic information and breast density has been shown to improve predictions for existing models, but detailed im
Externí odkaz:
https://doaj.org/article/4198fe48becf41f9bd916dfe91a7755f