Zobrazeno 1 - 10
of 61
pro vyhledávání: '"Qiao, Mengyun"'
Autor:
Zhang, Xinru, Ou, Ni, Basaran, Berke Doga, Visentin, Marco, Qiao, Mengyun, Gu, Renyang, Ouyang, Cheng, Liu, Yaou, Matthew, Paul M., Ye, Chuyang, Bai, Wenjia
Brain lesion segmentation plays an essential role in neurological research and diagnosis. As brain lesions can be caused by various pathological alterations, different types of brain lesions tend to manifest with different characteristics on differen
Externí odkaz:
http://arxiv.org/abs/2405.10246
Autor:
Wang, Fanwen, Tanzer, Michael, Qiao, Mengyun, Bai, Wenjia, Rueckert, Daniel, Yang, Guang, Nielles-Vallespin, Sonia
Quantitative cardiac magnetic resonance T1 and T2 mapping enable myocardial tissue characterisation but the lengthy scan times restrict their widespread clinical application. We propose a deep learning method that incorporates a time dependency Laten
Externí odkaz:
http://arxiv.org/abs/2309.16853
Autor:
Basaran, Berke Doga, Zhang, Weitong, Qiao, Mengyun, Kainz, Bernhard, Matthews, Paul M., Bai, Wenjia
Data augmentation has become a de facto component of deep learning-based medical image segmentation methods. Most data augmentation techniques used in medical imaging focus on spatial and intensity transformations to improve the diversity of training
Externí odkaz:
http://arxiv.org/abs/2308.09026
Autor:
Liu, Che, Cheng, Sibo, Chen, Chen, Qiao, Mengyun, Zhang, Weitong, Shah, Anand, Bai, Wenjia, Arcucci, Rossella
Medical vision-language models enable co-learning and integrating features from medical imaging and clinical text. However, these models are not easy to train and the latent representation space can be complex. Here we propose a novel way for pre-tra
Externí odkaz:
http://arxiv.org/abs/2307.08347
Autor:
Reynaud, Hadrien, Qiao, Mengyun, Dombrowski, Mischa, Day, Thomas, Razavi, Reza, Gomez, Alberto, Leeson, Paul, Kainz, Bernhard
Image synthesis is expected to provide value for the translation of machine learning methods into clinical practice. Fundamental problems like model robustness, domain transfer, causal modelling, and operator training become approachable through synt
Externí odkaz:
http://arxiv.org/abs/2303.12644
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
Understanding the intensity characteristics of brain lesions is key for defining image-based biomarkers in neurological studies and for predicting disease burden and outcome. In this work, we present a novel foreground-based generative method for mod
Externí odkaz:
http://arxiv.org/abs/2208.02135
This paper presents a fully automated method to segment the complex left atrial (LA) cavity, from 3D Gadolinium-enhanced magnetic resonance imaging (GE-MRI) scans. The proposed method consists of four steps: (1) preprocessing to convert the original
Externí odkaz:
http://arxiv.org/abs/1810.04425
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