Zobrazeno 1 - 10
of 403
pro vyhledávání: '"Eftestøl, Trygve"'
The most prevalent form of bladder cancer is urothelial carcinoma, characterized by a high recurrence rate and substantial lifetime treatment costs for patients. Grading is a prime factor for patient risk stratification, although it suffers from inco
Externí odkaz:
http://arxiv.org/abs/2405.15275
Autor:
Fuster, Saul, Khoraminia, Farbod, Silva-Rodríguez, Julio, Kiraz, Umay, van Leenders, Geert J. L. H., Eftestøl, Trygve, Naranjo, Valery, Janssen, Emiel A. M., Zuiverloon, Tahlita C. M., Engan, Kjersti
We present a pioneering investigation into the application of deep learning techniques to analyze histopathological images for addressing the substantial challenge of automated prognostic prediction. Prognostic prediction poses a unique challenge as
Externí odkaz:
http://arxiv.org/abs/2405.15264
Autor:
Rolfsnes, Erlend Sortland, Thangngat, Philip, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, Fernandez-Quilez, Alvaro
Magnetic resonance imaging has evolved as a key component for prostate cancer (PCa) detection, substantially increasing the radiologist workload. Artificial intelligence (AI) systems can support radiological assessment by segmenting and classifying l
Externí odkaz:
http://arxiv.org/abs/2309.08381
Autor:
Fernandez-Quilez, Alvaro, Vidziunas, Linas, Thoresen, Ørjan Kløvfjell, Oppedal, Ketil, Kjosavik, Svein Reidar, Eftestøl, Trygve
Traditional deep learning (DL) approaches based on supervised learning paradigms require large amounts of annotated data that are rarely available in the medical domain. Unsupervised Out-of-distribution (OOD) detection is an alternative that requires
Externí odkaz:
http://arxiv.org/abs/2308.06481
Leveraging multi-view data without annotations for prostate MRI segmentation: A contrastive approach
Autor:
Lindeijer, Tim Nikolass, Ytredal, Tord Martin, Eftestøl, Trygve, Nordström, Tobias, Jäderling, Fredrik, Eklund, Martin, Fernandez-Quilez, Alvaro
An accurate prostate delineation and volume characterization can support the clinical assessment of prostate cancer. A large amount of automatic prostate segmentation tools consider exclusively the axial MRI direction in spite of the availability as
Externí odkaz:
http://arxiv.org/abs/2308.06477
Computational Pathology (CPATH) systems have the potential to automate diagnostic tasks. However, the artifacts on the digitized histological glass slides, known as Whole Slide Images (WSIs), may hamper the overall performance of CPATH systems. Deep
Externí odkaz:
http://arxiv.org/abs/2305.17370
Autor:
Meinich-Bache, Øyvind, Engan, Kjersti, Austvoll, Ivar, Eftestøl, Trygve, Myklebust, Helge, Yarrot, Ladislaus Blacy, Kidanto, Hussein, Ersdal, Hege
Publikováno v:
IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 3, pp. 796-803, March 2020
Birth asphyxia is a major newborn mortality problem in low-resource countries. International guideline provides treatment recommendations; however, the importance and effect of the different treatments are not fully explored. The available data is co
Externí odkaz:
http://arxiv.org/abs/2303.07790
Autor:
Meinich-Bache, Øyvind, Austnes, Simon Lennart, Engan, Kjersti, Austvoll, Ivar, Eftestøl, Trygve, Myklebust, Helge, Kusulla, Simeon, Kidanto, Hussein, Ersdal, Hege
Publikováno v:
IEEE Journal of Biomedical and Health Informatics, vol. 24, no. 11, pp. 3258-3267, Nov. 2020
Objective: Birth asphyxia is one of the leading causes of neonatal deaths. A key for survival is performing immediate and continuous quality newborn resuscitation. A dataset of recorded signals during newborn resuscitation, including videos, has been
Externí odkaz:
http://arxiv.org/abs/2303.07789
Autor:
Fuster, Saul, Khoraminia, Farbod, Eftestøl, Trygve, Zuiverloon, Tahlita C. M., Engan, Kjersti
Accurate segmentation of tissue in histopathological images can be very beneficial for defining regions of interest (ROI) for streamline of diagnostic and prognostic tasks. Still, adapting to different domains is essential for histopathology image an
Externí odkaz:
http://arxiv.org/abs/2303.05225
Autor:
Fernandez-Quilez, Alvaro, Andersen, Christoffer Gabrielsen, Eftestøl, Trygve, Kjosavik, Svein Reidar, Oppedal, Ketil
Masked Image Modelling (MIM) has been shown to be an efficient self-supervised learning (SSL) pre-training paradigm when paired with transformer architectures and in the presence of a large amount of unlabelled natural images. The combination of the
Externí odkaz:
http://arxiv.org/abs/2212.14267