Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Robben, David"'
Autor:
de la Rosa, Ezequiel, Reyes, Mauricio, Liew, Sook-Lei, Hutton, Alexandre, Wiest, Roland, Kaesmacher, Johannes, Hanning, Uta, Hakim, Arsany, Zubal, Richard, Valenzuela, Waldo, Robben, David, Sima, Diana M., Anania, Vincenzo, Brys, Arne, Meakin, James A., Mickan, Anne, Broocks, Gabriel, Heitkamp, Christian, Gao, Shengbo, Liang, Kongming, Zhang, Ziji, Siddiquee, Md Mahfuzur Rahman, Myronenko, Andriy, Ashtari, Pooya, Van Huffel, Sabine, Jeong, Hyun-su, Yoon, Chi-ho, Kim, Chulhong, Huo, Jiayu, Ourselin, Sebastien, Sparks, Rachel, Clèrigues, Albert, Oliver, Arnau, Lladó, Xavier, Chalcroft, Liam, Pappas, Ioannis, Bertels, Jeroen, Heylen, Ewout, Moreau, Juliette, Hatami, Nima, Frindel, Carole, Qayyum, Abdul, Mazher, Moona, Puig, Domenec, Lin, Shao-Chieh, Juan, Chun-Jung, Hu, Tianxi, Boone, Lyndon, Goubran, Maged, Liu, Yi-Jui, Wegener, Susanne, Kofler, Florian, Ezhov, Ivan, Shit, Suprosanna, Petzsche, Moritz R. Hernandez, Menze, Bjoern, Kirschke, Jan S., Wiestler, Benedikt
Diffusion-weighted MRI (DWI) is essential for stroke diagnosis, treatment decisions, and prognosis. However, image and disease variability hinder the development of generalizable AI algorithms with clinical value. We address this gap by presenting a
Externí odkaz:
http://arxiv.org/abs/2403.19425
We know that both the CNN mapping function and the sampling scheme are of paramount importance for CNN-based image analysis. It is clear that both functions operate in the same space, with an image axis $\mathcal{I}$ and a feature axis $\mathcal{F}$.
Externí odkaz:
http://arxiv.org/abs/2211.09569
In this article, we look into some essential aspects of convolutional neural networks (CNNs) with the focus on medical image segmentation. First, we discuss the CNN architecture, thereby highlighting the spatial origin of the data, voxel-wise classif
Externí odkaz:
http://arxiv.org/abs/2211.09562
This article focuses on the control center of each human body: the brain. We will point out the pivotal role of the cerebral vasculature and how its complex mechanisms may vary between subjects. We then emphasize a specific acute pathological state,
Externí odkaz:
http://arxiv.org/abs/2211.04850
Publikováno v:
Elsevier Medical Image Analysis, Volume 61, January 2021, 101833
The clinical interest is often to measure the volume of a structure, which is typically derived from a segmentation. In order to evaluate and compare segmentation methods, the similarity between a segmentation and a predefined ground truth is measure
Externí odkaz:
http://arxiv.org/abs/2211.04161
Publikováno v:
Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022. Lecture Notes in Computer Science, vol 13435. Springer, Cham
Albeit the Dice loss is one of the dominant loss functions in medical image segmentation, most research omits a closer look at its derivative, i.e. the real motor of the optimization when using gradient descent. In this paper, we highlight the peculi
Externí odkaz:
http://arxiv.org/abs/2207.09521
Autor:
Petzsche, Moritz Roman Hernandez, de la Rosa, Ezequiel, Hanning, Uta, Wiest, Roland, Pinilla, Waldo Enrique Valenzuela, Reyes, Mauricio, Meyer, Maria Ines, Liew, Sook-Lei, Kofler, Florian, Ezhov, Ivan, Robben, David, Hutton, Alexander, Friedrich, Tassilo, Zarth, Teresa, Bürkle, Johannes, Baran, The Anh, Menze, Bjoern, Broocks, Gabriel, Meyer, Lukas, Zimmer, Claus, Boeckh-Behrens, Tobias, Berndt, Maria, Ikenberg, Benno, Wiestler, Benedikt, Kirschke, Jan S.
Publikováno v:
Scientific data 9.1 (2022): 762
Magnetic resonance imaging (MRI) is a central modality for stroke imaging. It is used upon patient admission to make treatment decisions such as selecting patients for intravenous thrombolysis or endovascular therapy. MRI is later used in the duratio
Externí odkaz:
http://arxiv.org/abs/2206.06694
Publikováno v:
International Conference on Medical Image Computing and Computer-Assisted Intervention 2020 Oct 4 (pp. 593-602)
Perfusion imaging is the current gold standard for acute ischemic stroke analysis. It allows quantification of the salvageable and non-salvageable tissue regions (penumbra and core areas respectively). In clinical settings, the singular value decompo
Externí odkaz:
http://arxiv.org/abs/2103.17111
Explainable-by-design Semi-Supervised Representation Learning for COVID-19 Diagnosis from CT Imaging
Autor:
Berenguer, Abel Díaz, Sahli, Hichem, Joukovsky, Boris, Kvasnytsia, Maryna, Dirks, Ine, Alioscha-Perez, Mitchel, Deligiannis, Nikos, Gonidakis, Panagiotis, Sánchez, Sebastián Amador, Brahimetaj, Redona, Papavasileiou, Evgenia, Chana, Jonathan Cheung-Wai, Li, Fei, Song, Shangzhen, Yang, Yixin, Tilborghs, Sofie, Willems, Siri, Eelbode, Tom, Bertels, Jeroen, Vandermeulen, Dirk, Maes, Frederik, Suetens, Paul, Fidon, Lucas, Vercauteren, Tom, Robben, David, Brys, Arne, Smeets, Dirk, Ilsen, Bart, Buls, Nico, Watté, Nina, de Mey, Johan, Snoeckx, Annemiek, Parizel, Paul M., Guiot, Julien, Deprez, Louis, Meunier, Paul, Gryspeerdt, Stefaan, De Smet, Kristof, Jansen, Bart, Vandemeulebroucke, Jef
Our motivating application is a real-world problem: COVID-19 classification from CT imaging, for which we present an explainable Deep Learning approach based on a semi-supervised classification pipeline that employs variational autoencoders to extrac
Externí odkaz:
http://arxiv.org/abs/2011.11719
Publikováno v:
In: Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries. BrainLes 2020. Lecture Notes in Computer Science, vol 12658. Springer, Cham (2021)
Anomaly detection (AD) is the identification of data samples that do not fit a learned data distribution. As such, AD systems can help physicians to determine the presence, severity, and extension of a pathology. Deep generative models, such as Gener
Externí odkaz:
http://arxiv.org/abs/2010.04717