Zobrazeno 1 - 10
of 93
pro vyhledávání: '"Møllersen, Kajsa"'
Autor:
Tafavvoghi, Masoud, Sildnes, Anders, Rakaee, Mehrdad, Shvetsov, Nikita, Bongo, Lars Ailo, Busund, Lill-Tove Rasmussen, Møllersen, Kajsa
Classifying breast cancer molecular subtypes is crucial for tailoring treatment strategies. While immunohistochemistry (IHC) and gene expression profiling are standard methods for molecular subtyping, IHC can be subjective, and gene profiling is cost
Externí odkaz:
http://arxiv.org/abs/2409.09053
Autor:
Shvetsov, Nikita, Sildnes, Anders, Busund, Lill-Tove Rasmussen, Dalen, Stig, Møllersen, Kajsa, Bongo, Lars Ailo, Kilvaer, Thomas K.
Addressing the critical need for accurate prognostic biomarkers in cancer treatment, quantifying tumor-infiltrating lymphocytes (TILs) in non-small cell lung cancer (NSCLC) presents considerable challenges. Manual TIL quantification in whole slide im
Externí odkaz:
http://arxiv.org/abs/2405.02913
Autor:
Tafavvoghi, Masoud, Bongo, Lars Ailo, Shvetsov, Nikita, Busund, Lill-Tove Rasmussen, Møllersen, Kajsa
Advancements in digital pathology and computing resources have made a significant impact in the field of computational pathology for breast cancer diagnosis and treatment. However, access to high-quality labeled histopathological images of breast can
Externí odkaz:
http://arxiv.org/abs/2306.01546
Autor:
Møllersen, Kajsa, Holsbø, Einar
Machine learning methods are commonly evaluated and compared by their performance on data sets from public repositories. This allows for multiple methods, oftentimes several thousands, to be evaluated under identical conditions and across time. The h
Externí odkaz:
http://arxiv.org/abs/2303.07272
Autor:
Shvetsov, Nikita, Grønnesby, Morten, Pedersen, Edvard, Møllersen, Kajsa, Busund, Lill-Tove Rasmussen, Schwienbacher, Ruth, Bongo, Lars Ailo, Kilvaer, Thomas K.
Publikováno v:
Cancers, 14 (2022) 12, 2974
Increased levels of tumor infiltrating lymphocytes (TILs) in cancer tissue indicate favourable outcomes in many types of cancer. Manual quantification of immune cells is inaccurate and time consuming for pathologists. Our aim is to leverage a computa
Externí odkaz:
http://arxiv.org/abs/2202.06590
Autor:
Tafavvoghi, Masoud, Bongo, Lars Ailo, Shvetsov, Nikita, Busund, Lill-Tove Rasmussen, Møllersen, Kajsa
Publikováno v:
In Journal of Pathology Informatics December 2024 15
Foraminifera are single-celled marine organisms that construct shells that remain as fossils in the marine sediments. Classifying and counting these fossils are important in e.g. paleo-oceanographic and -climatological research. However, the identifi
Externí odkaz:
http://arxiv.org/abs/2105.14191
Replication studies are essential for validation of new methods, and are crucial to maintain the high standards of scientific publications, and to use the results in practice. We have attempted to replicate the main method in 'Development and validat
Externí odkaz:
http://arxiv.org/abs/1803.04337
In multi-instance (MI) learning, each object (bag) consists of multiple feature vectors (instances), and is most commonly regarded as a set of points in a multidimensional space. A different viewpoint is that the instances are realisations of random
Externí odkaz:
http://arxiv.org/abs/1803.02782
Publikováno v:
PLoS ONE 12(12): e0190112, 2017
Melanoma is the deadliest form of skin cancer. Computer systems can assist in melanoma detection, but are not widespread in clinical practice. In 2016, an open challenge in classification of dermoscopic images of skin lesions was announced. A trainin
Externí odkaz:
http://arxiv.org/abs/1802.01301