Zobrazeno 1 - 10
of 66
pro vyhledávání: '"Jose Dolz"'
Publikováno v:
Scientific Reports, Vol 13, Iss 1, Pp 1-11 (2023)
Abstract Neonatal MRIs are used increasingly in preterm infants. However, it is not always feasible to analyze this data. Having a tool that assesses brain maturation during this period of extraordinary changes would be immensely helpful. Approaches
Externí odkaz:
https://doaj.org/article/55a929f521894ccea82e1344608765fa
Autor:
Yang Ding, Rolando Acosta, Vicente Enguix, Sabrina Suffren, Janosch Ortmann, David Luck, Jose Dolz, Gregory A. Lodygensky
Publikováno v:
Frontiers in Neuroscience, Vol 14 (2020)
IntroductionDeep learning neural networks are especially potent at dealing with structured data, such as images and volumes. Both modified LiviaNET and HyperDense-Net performed well at a prior competition segmenting 6-month-old infant magnetic resona
Externí odkaz:
https://doaj.org/article/f7e4ae9ccad649a8adaf1509cd364fc8
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. 26:4599-4610
Learning similarity is a key aspect in medical image analysis, particularly in recommendation systems or in uncovering the interpretation of anatomical data in images. Most existing methods learn such similarities in the embedding space over image se
Autor:
Julien Nicolas, Kazem Gumus, Seyed Behzad Jazayeri, Deheeraj Reddy Gopireddy, Jose Dolz, Mark Bandyk
Publikováno v:
Journal of Urology. 209
Publikováno v:
RiuNet. Repositorio Institucional de la Universitat Politécnica de Valéncia
instname
instname
[EN] Prostate cancer is one of the main diseases affecting men worldwide. The gold standard for diagnosis and prognosis is the Gleason grading system. In this process, pathologists manually analyze prostate histology slides under microscope, in a hig
Autor:
Jose Dolz, Ashish Sinha
Publikováno v:
IEEE Journal of Biomedical and Health Informatics. 25:121-130
Even though convolutional neural networks (CNNs) are driving progress in medical image segmentation, standard models still have some drawbacks. First, the use of multi-scale approaches, i.e., encoder-decoder architectures, leads to a redundant use of
Current unsupervised anomaly localization approaches rely on generative models to learn the distribution of normal images, which is later used to identify potential anomalous regions derived from errors on the reconstructed images. However, a main li
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::d74e151d6006d2bdac1242ec75cf4a43
http://arxiv.org/abs/2203.01671
http://arxiv.org/abs/2203.01671
Despite the undeniable progress in visual recognition tasks fueled by deep neural networks, there exists recent evidence showing that these models are poorly calibrated, resulting in over-confident predictions. The standard practices of minimizing th
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::141240e1e09fdc51780948326bbf8825
Publikováno v:
Uncertainty for Safe Utilization of Machine Learning in Medical Imaging ISBN: 9783031167485
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::fdfe971696bcb9d88073ba46c674015a
https://doi.org/10.1007/978-3-031-16749-2_4
https://doi.org/10.1007/978-3-031-16749-2_4
Publikováno v:
Lecture Notes in Computer Science ISBN: 9783031164514
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::4a350875dcbcac5102af76db0fe8f57a
https://doi.org/10.1007/978-3-031-16452-1_26
https://doi.org/10.1007/978-3-031-16452-1_26