Zobrazeno 1 - 10
of 104
pro vyhledávání: '"Baust, Maximilian"'
Autor:
Bonev, Boris, Kurth, Thorsten, Hundt, Christian, Pathak, Jaideep, Baust, Maximilian, Kashinath, Karthik, Anandkumar, Anima
Fourier Neural Operators (FNOs) have proven to be an efficient and effective method for resolution-independent operator learning in a broad variety of application areas across scientific machine learning. A key reason for their success is their abili
Externí odkaz:
http://arxiv.org/abs/2306.03838
Autor:
Rieke, Nicola, Hancox, Jonny, Li, Wenqi, Milletari, Fausto, Roth, Holger, Albarqouni, Shadi, Bakas, Spyridon, Galtier, Mathieu N., Landman, Bennett, Maier-Hein, Klaus, Ourselin, Sebastien, Sheller, Micah, Summers, Ronald M., Trask, Andrew, Xu, Daguang, Baust, Maximilian, Cardoso, M. Jorge
Publikováno v:
npj Digital Medicine volume 3, Article number: 119 (2020)
Data-driven Machine Learning has emerged as a promising approach for building accurate and robust statistical models from medical data, which is collected in huge volumes by modern healthcare systems. Existing medical data is not fully exploited by M
Externí odkaz:
http://arxiv.org/abs/2003.08119
Autor:
Li, Wenqi, Milletarì, Fausto, Xu, Daguang, Rieke, Nicola, Hancox, Jonny, Zhu, Wentao, Baust, Maximilian, Cheng, Yan, Ourselin, Sébastien, Cardoso, M. Jorge, Feng, Andrew
Due to medical data privacy regulations, it is often infeasible to collect and share patient data in a centralised data lake. This poses challenges for training machine learning algorithms, such as deep convolutional networks, which often require lar
Externí odkaz:
http://arxiv.org/abs/1910.00962
Freehand three-dimensional ultrasound (3D-US) has gained considerable interest in research, but even today suffers from its high inter-operator variability in clinical practice. The high variability mainly arises from tracking inaccuracies as well as
Externí odkaz:
http://arxiv.org/abs/1811.01534
Autor:
Aresta, Guilherme, Araújo, Teresa, Kwok, Scotty, Chennamsetty, Sai Saketh, Safwan, Mohammed, Alex, Varghese, Marami, Bahram, Prastawa, Marcel, Chan, Monica, Donovan, Michael, Fernandez, Gerardo, Zeineh, Jack, Kohl, Matthias, Walz, Christoph, Ludwig, Florian, Braunewell, Stefan, Baust, Maximilian, Vu, Quoc Dang, To, Minh Nguyen Nhat, Kim, Eal, Kwak, Jin Tae, Galal, Sameh, Sanchez-Freire, Veronica, Brancati, Nadia, Frucci, Maria, Riccio, Daniel, Wang, Yaqi, Sun, Lingling, Ma, Kaiqiang, Fang, Jiannan, Kone, Ismael, Boulmane, Lahsen, Campilho, Aurélio, Eloy, Catarina, Polónia, António, Aguiar, Paulo
Publikováno v:
Medical Image Analysis, 2019
Breast cancer is the most common invasive cancer in women, affecting more than 10% of women worldwide. Microscopic analysis of a biopsy remains one of the most important methods to diagnose the type of breast cancer. This requires specialized analysi
Externí odkaz:
http://arxiv.org/abs/1808.04277
Autor:
Rackerseder, Julia, Baust, Maximilian, Göbl, Rüdiger, Navab, Nassir, Hennersperger, Christoph
Registration of partial-view 3D US volumes with MRI data is influenced by initialization. The standard of practice is using extrinsic or intrinsic landmarks, which can be very tedious to obtain. To overcome the limitations of registration initializat
Externí odkaz:
http://arxiv.org/abs/1806.04368
Recent neural-network-based architectures for image segmentation make extensive usage of feature forwarding mechanisms to integrate information from multiple scales. Although yielding good results, even deeper architectures and alternative methods fo
Externí odkaz:
http://arxiv.org/abs/1806.01413
Variational methods for revealing visual concepts learned by convolutional neural networks have gained significant attention during the last years. Being based on noisy gradients obtained via back-propagation such methods require the application of r
Externí odkaz:
http://arxiv.org/abs/1805.00071
Breast cancer is the most frequently diagnosed cancer and leading cause of cancer-related death among females worldwide. In this article, we investigate the applicability of densely connected convolutional neural networks to the problems of histology
Externí odkaz:
http://arxiv.org/abs/1804.04595
Autor:
Rupprecht, Christian, Laina, Iro, DiPietro, Robert, Baust, Maximilian, Tombari, Federico, Navab, Nassir, Hager, Gregory D.
Many prediction tasks contain uncertainty. In some cases, uncertainty is inherent in the task itself. In future prediction, for example, many distinct outcomes are equally valid. In other cases, uncertainty arises from the way data is labeled. For ex
Externí odkaz:
http://arxiv.org/abs/1612.00197