Zobrazeno 1 - 10
of 1 413
pro vyhledávání: '"A. Jimenez-Sanchez"'
Autor:
Sourget, Théo, Hestbek-Møller, Michelle, Jiménez-Sánchez, Amelia, Xu, Jack Junchi, Cheplygina, Veronika
The development of larger models for medical image analysis has led to increased performance. However, it also affected our ability to explain and validate model decisions. Models can use non-relevant parts of images, also called spurious correlation
Externí odkaz:
http://arxiv.org/abs/2412.04030
We develop a semi-analytical model for transport in structured catalytic microreactors, where both reactant and product are compressible fluids. Making use of the lubrication and Fick-Jacobs approximations, we reduce the three-dimensional governing e
Externí odkaz:
http://arxiv.org/abs/2407.03944
Autor:
Juodelyte, Dovile, Lu, Yucheng, Jiménez-Sánchez, Amelia, Bottazzi, Sabrina, Ferrante, Enzo, Cheplygina, Veronika
Transfer learning has become an essential part of medical imaging classification algorithms, often leveraging ImageNet weights. The domain shift from natural to medical images has prompted alternatives such as RadImageNet, often showing comparable cl
Externí odkaz:
http://arxiv.org/abs/2403.04484
Autor:
Jiménez-Sánchez, Juan, Ortega-Sabater, Carmen, Maini, Philip K., Pérez-García, Víctor M., Lorenzi, Tommaso
Evolvability is defined as the ability of a population to generate heritable variation to facilitate its adaptation to new environments or selection pressures. In this article, we consider evolvability as a phenotypic trait subject to evolution and d
Externí odkaz:
http://arxiv.org/abs/2402.06392
Autor:
Jiménez-Sánchez, Amelia, Avlona, Natalia-Rozalia, Juodelyte, Dovile, Sourget, Théo, Vang-Larsen, Caroline, Rogers, Anna, Zając, Hubert Dariusz, Cheplygina, Veronika
Medical Imaging (MI) datasets are fundamental to artificial intelligence in healthcare. The accuracy, robustness, and fairness of diagnostic algorithms depend on the data (and its quality) used to train and evaluate the models. MI datasets used to be
Externí odkaz:
http://arxiv.org/abs/2402.06353
Autor:
Sourget, Théo, Akkoç, Ahmet, Winther, Stinna, Galsgaard, Christine Lyngbye, Jiménez-Sánchez, Amelia, Juodelyte, Dovile, Petitjean, Caroline, Cheplygina, Veronika
Medical imaging papers often focus on methodology, but the quality of the algorithms and the validity of the conclusions are highly dependent on the datasets used. As creating datasets requires a lot of effort, researchers often use publicly availabl
Externí odkaz:
http://arxiv.org/abs/2402.03003
Autor:
Sofia Jimenez-Sanchez, Rebekah Maksoud, Natalie Eaton-Fitch, Sonya Marshall-Gradisnik, Simon A. Broadley
Publikováno v:
Journal of Neuroinflammation, Vol 21, Iss 1, Pp 1-12 (2024)
Abstract Background Secondary autoimmune disease (SAID) in the context of alemtuzumab treatment is one of the main safety concerns that may arise following administration in people with multiple sclerosis (pwMS). Contributing factors underlying this
Externí odkaz:
https://doaj.org/article/e9de96c826844292bf527c52ae9673ae
Autor:
Damgaard, Cathrine, Eriksen, Trine Naja, Juodelyte, Dovile, Cheplygina, Veronika, Jiménez-Sánchez, Amelia
The advancement of machine learning algorithms in medical image analysis requires the expansion of training datasets. A popular and cost-effective approach is automated annotation extraction from free-text medical reports, primarily due to the high c
Externí odkaz:
http://arxiv.org/abs/2309.02244
While a key component to the success of deep learning is the availability of massive amounts of training data, medical image datasets are often limited in diversity and size. Transfer learning has the potential to bridge the gap between related yet d
Externí odkaz:
http://arxiv.org/abs/2302.08272
Publikováno v:
Autophagy Reports, Vol 3, Iss 1 (2024)
Externí odkaz:
https://doaj.org/article/2b288fcdbef24653b6354a24b830f7bc