Zobrazeno 1 - 10
of 52
pro vyhledávání: '"Qiu, Huaqi"'
Autor:
Sideri-Lampretsa, Vasiliki, Zimmer, Veronika A., Qiu, Huaqi, Kaissis, Georgios, Rueckert, Daniel
Multi-modal image registration is a crucial pre-processing step in many medical applications. However, it is a challenging task due to the complex intensity relationships between different imaging modalities, which can result in large discrepancy in
Externí odkaz:
http://arxiv.org/abs/2309.02875
Deep learning-based image registration approaches have shown competitive performance and run-time advantages compared to conventional image registration methods. However, existing learning-based approaches mostly require to train separate models with
Externí odkaz:
http://arxiv.org/abs/2303.10700
Autor:
Qiao, Mengyun, Wang, Shuo, Qiu, Huaqi, de Marvao, Antonio, O'Regan, Declan P., Rueckert, Daniel, Bai, Wenjia
Two key questions in cardiac image analysis are to assess the anatomy and motion of the heart from images; and to understand how they are associated with non-imaging clinical factors such as gender, age and diseases. While the first question can ofte
Externí odkaz:
http://arxiv.org/abs/2301.13098
Autor:
Qiao, Mengyun, Basaran, Berke Doga, Qiu, Huaqi, Wang, Shuo, Guo, Yi, Wang, Yuanyuan, Matthews, Paul M., Rueckert, Daniel, Bai, Wenjia
Cardiovascular disease, the leading cause of death globally, is an age-related disease. Understanding the morphological and functional changes of the heart during ageing is a key scientific question, the answer to which will help us define important
Externí odkaz:
http://arxiv.org/abs/2208.13146
Autor:
Hering, Alessa, Hansen, Lasse, Mok, Tony C. W., Chung, Albert C. S., Siebert, Hanna, Häger, Stephanie, Lange, Annkristin, Kuckertz, Sven, Heldmann, Stefan, Shao, Wei, Vesal, Sulaiman, Rusu, Mirabela, Sonn, Geoffrey, Estienne, Théo, Vakalopoulou, Maria, Han, Luyi, Huang, Yunzhi, Yap, Pew-Thian, Brudfors, Mikael, Balbastre, Yaël, Joutard, Samuel, Modat, Marc, Lifshitz, Gal, Raviv, Dan, Lv, Jinxin, Li, Qiang, Jaouen, Vincent, Visvikis, Dimitris, Fourcade, Constance, Rubeaux, Mathieu, Pan, Wentao, Xu, Zhe, Jian, Bailiang, De Benetti, Francesca, Wodzinski, Marek, Gunnarsson, Niklas, Sjölund, Jens, Grzech, Daniel, Qiu, Huaqi, Li, Zeju, Thorley, Alexander, Duan, Jinming, Großbröhmer, Christoph, Hoopes, Andrew, Reinertsen, Ingerid, Xiao, Yiming, Landman, Bennett, Huo, Yuankai, Murphy, Keelin, Lessmann, Nikolas, van Ginneken, Bram, Dalca, Adrian V., Heinrich, Mattias P.
Image registration is a fundamental medical image analysis task, and a wide variety of approaches have been proposed. However, only a few studies have comprehensively compared medical image registration approaches on a wide range of clinically releva
Externí odkaz:
http://arxiv.org/abs/2112.04489
Deep learning (DL) image registration methods amortize the costly pair-wise iterative optimization by training deep neural networks to predict the optimal transformation in one fast forward-pass. In this work, we bridge the gap between traditional it
Externí odkaz:
http://arxiv.org/abs/2112.03915
Autor:
Grzech, Daniel, Azampour, Mohammad Farid, Qiu, Huaqi, Glocker, Ben, Kainz, Bernhard, Folgoc, Loïc Le
We develop a new Bayesian model for non-rigid registration of three-dimensional medical images, with a focus on uncertainty quantification. Probabilistic registration of large images with calibrated uncertainty estimates is difficult for both computa
Externí odkaz:
http://arxiv.org/abs/2110.13289
Autor:
Folgoc, Loic Le, Baltatzis, Vasileios, Desai, Sujal, Devaraj, Anand, Ellis, Sam, Manzanera, Octavio E. Martinez, Nair, Arjun, Qiu, Huaqi, Schnabel, Julia, Glocker, Ben
MC Dropout is a mainstream "free lunch" method in medical imaging for approximate Bayesian computations (ABC). Its appeal is to solve out-of-the-box the daunting task of ABC and uncertainty quantification in Neural Networks (NNs); to fall within the
Externí odkaz:
http://arxiv.org/abs/2110.04286
Autor:
Chen, Chen, Qin, Chen, Ouyang, Cheng, Li, Zeju, Wang, Shuo, Qiu, Huaqi, Chen, Liang, Tarroni, Giacomo, Bai, Wenjia, Rueckert, Daniel
The success of neural networks on medical image segmentation tasks typically relies on large labeled datasets for model training. However, acquiring and manually labeling a large medical image set is resource-intensive, expensive, and sometimes impra
Externí odkaz:
http://arxiv.org/abs/2108.03429
Autor:
Jia, Xi, Thorley, Alexander, Chen, Wei, Qiu, Huaqi, Shen, Linlin, Styles, Iain B, Chang, Hyung Jin, Leonardis, Ales, de Marvao, Antonio, O'Regan, Declan P., Rueckert, Daniel, Duan, Jinming
Data-driven deep learning approaches to image registration can be less accurate than conventional iterative approaches, especially when training data is limited. To address this whilst retaining the fast inference speed of deep learning, we propose V
Externí odkaz:
http://arxiv.org/abs/2105.12227