Zobrazeno 1 - 10
of 64
pro vyhledávání: '"Claes Lundström"'
Publikováno v:
Frontiers in Bioinformatics, Vol 3 (2023)
In this perspective article we discuss a certain type of research on visualization for bioinformatics data, namely, methods targeting clinical use. We argue that in this subarea additional complex challenges come into play, particularly so in genomic
Externí odkaz:
https://doaj.org/article/8a3e8e20092e41e6bd9fad461fe99eca
Publikováno v:
Scientific Reports, Vol 12, Iss 1, Pp 1-15 (2022)
Abstract Deep learning (DL) has shown great potential in digital pathology applications. The robustness of a diagnostic DL-based solution is essential for safe clinical deployment. In this work we evaluate if adding uncertainty estimates for DL predi
Externí odkaz:
https://doaj.org/article/136a8712cbfe449da4d5cdce3b0eeacb
Autor:
Jakub Olczak, John Pavlopoulos, Jasper Prijs, Frank F A Ijpma, Job N Doornberg, Claes Lundström, Joel Hedlund, Max Gordon
Publikováno v:
Acta Orthopaedica, Vol 92, Iss 5, Pp 513-525 (2021)
Background and purpose — Artificial intelligence (AI), deep learning (DL), and machine learning (ML) have become common research fields in orthopedics and medicine in general. Engineers perform much of the work. While they gear the results towards
Externí odkaz:
https://doaj.org/article/d5f89633677b492791e7c073220f9c52
Autor:
André Homeyer, Patrik Nasr, Christiane Engel, Stergios Kechagias, Peter Lundberg, Mattias Ekstedt, Henning Kost, Nick Weiss, Tim Palmer, Horst Karl Hahn, Darren Treanor, Claes Lundström
Publikováno v:
Diagnostic Pathology, Vol 12, Iss 1, Pp 1-10 (2017)
Abstract Background Steatosis is routinely assessed histologically in clinical practice and research. Automated image analysis can reduce the effort of quantifying steatosis. Since reproducibility is essential for practical use, we have evaluated dif
Externí odkaz:
https://doaj.org/article/48c031e72778412b8c262afebf33acce
Autor:
Martin Lindvall, Alexander Sanner, Fredrik Petré, Karin Lindman, Darren Treanor, Claes Lundström, Jonas Löwgren
Publikováno v:
Journal of Pathology Informatics, Vol 11, Iss 1, Pp 27-27 (2020)
Background: Recent advancements in machine learning (ML) bring great possibilities for the development of tools to assist with diagnostic tasks within histopathology. However, these approaches typically require a large amount of ground truth training
Externí odkaz:
https://doaj.org/article/150934ae7bb14fcb9b622a5b6f3a0190
Autor:
Sylvia L Asa, Anna C Bodén, Darren Treanor, Sofia Jarkman, Claes Lundström, Liron Pantanowitz
Publikováno v:
Journal of Pathology Informatics, Vol 10, Iss 1, Pp 27-27 (2019)
Externí odkaz:
https://doaj.org/article/11cd5c6f309748a19a4df28a649cc9f1
Publikováno v:
Journal of Pathology Informatics, Vol 8, Iss 1, Pp 21-21 (2017)
Background: Generating good training datasets is essential for machine learning-based nuclei detection methods. However, creating exhaustive nuclei contour annotations, to derive optimal training data from, is often infeasible. Methods: We compared d
Externí odkaz:
https://doaj.org/article/ee9b4bc21c6a4465a10fbd3182bd6f0f
Publikováno v:
Journal of Pathology Informatics, Vol 8, Iss 1, Pp 8-8 (2017)
The Nordic symposium on digital pathology (NDP) was created to promote knowledge exchange across stakeholders in health care, industry, and academia. In 2016, the 4th NDP installment took place in Linköping, Sweden, promoting development and collabo
Externí odkaz:
https://doaj.org/article/1653ad120dc343bfa025cc76d76b554b
Publikováno v:
Journal of Pathology Informatics, Vol 6, Iss 1, Pp 5-5 (2015)
Techniques for digital pathology are envisioned to provide great benefits in clinical practice, but experiences also show that solutions must be carefully crafted. The Nordic countries are far along the path toward the use of whole-slide imaging in c
Externí odkaz:
https://doaj.org/article/7c06021406d1462da697abb290862565
Publikováno v:
Journal of Pathology Informatics, Vol 6, Iss 1, Pp 7-7 (2015)
This paper describes work presented at the Nordic Symposium on Digital Pathology 2014, Linköping, Sweden. Quick and seamless integration between input devices and the navigation of digital slides remains a key barrier for many pathologists to "go di
Externí odkaz:
https://doaj.org/article/4a2948a710e94c478aed6a0d5f797e0a