Zobrazeno 1 - 10
of 169
pro vyhledávání: '"Dou, Haoran"'
Autor:
Han, Luyi, Tan, Tao, Zhang, Tianyu, Wang, Xin, Gao, Yuan, Lu, Chunyao, Liang, Xinglong, Dou, Haoran, Huang, Yunzhi, Mann, Ritse
Adversarial learning helps generative models translate MRI from source to target sequence when lacking paired samples. However, implementing MRI synthesis with adversarial learning in clinical settings is challenging due to training instability and m
Externí odkaz:
http://arxiv.org/abs/2407.02911
Echocardiography (ECHO) video is widely used for cardiac examination. In clinical, this procedure heavily relies on operator experience, which needs years of training and maybe the assistance of deep learning-based systems for enhanced accuracy and e
Externí odkaz:
http://arxiv.org/abs/2406.14098
Autor:
Lin, Fengming, Xia, Yan, MacRaild, Michael, Deo, Yash, Dou, Haoran, Liu, Qiongyao, Cheng, Nina, Ravikumar, Nishant, Frangi, Alejandro F.
The automated segmentation of cerebral aneurysms is pivotal for accurate diagnosis and treatment planning. Confronted with significant domain shifts and class imbalance in 3D Rotational Angiography (3DRA) data from various medical institutions, the t
Externí odkaz:
http://arxiv.org/abs/2402.15239
Autor:
Lin, Fengming, Xia, Yan, MacRaild, Michael, Deo, Yash, Dou, Haoran, Liu, Qiongyao, Wu, Kun, Ravikumar, Nishant, Frangi, Alejandro F.
Unsupervised domain adaptation (UDA) aims to align the labelled source distribution with the unlabelled target distribution to obtain domain-invariant predictive models. Since cross-modality medical data exhibit significant intra and inter-domain shi
Externí odkaz:
http://arxiv.org/abs/2402.15237
Autor:
Deo, Yash, Bonazzola, Rodrigo, Dou, Haoran, Xia, Yan, Wei, Tianyou, Ravikumar, Nishant, Frangi, Alejandro F., Lassila, Toni
Magnetic resonance angiography (MRA) is an imaging modality for visualising blood vessels. It is useful for several diagnostic applications and for assessing the risk of adverse events such as haemorrhagic stroke (resulting from the rupture of aneury
Externí odkaz:
http://arxiv.org/abs/2308.12861
The Circle of Willis (CoW) is the part of cerebral vasculature responsible for delivering blood to the brain. Understanding the diverse anatomical variations and configurations of the CoW is paramount to advance research on cerebrovascular diseases a
Externí odkaz:
http://arxiv.org/abs/2308.06781
Autor:
Han, Luyi, Zhang, Tianyu, Huang, Yunzhi, Dou, Haoran, Wang, Xin, Gao, Yuan, Lu, Chunyao, Tao, Tan, Mann, Ritse
Multi-sequence MRI is valuable in clinical settings for reliable diagnosis and treatment prognosis, but some sequences may be unusable or missing for various reasons. To address this issue, MRI synthesis is a potential solution. Recent deep learning-
Externí odkaz:
http://arxiv.org/abs/2307.00885
Autor:
Dou, Haoran, Bi, Ning, Han, Luyi, Huang, Yuhao, Mann, Ritse, Yang, Xin, Ni, Dong, Ravikumar, Nishant, Frangi, Alejandro F., Huang, Yunzhi
Deep learning-based deformable registration methods have been widely investigated in diverse medical applications. Learning-based deformable registration relies on weighted objective functions trading off registration accuracy and smoothness of the d
Externí odkaz:
http://arxiv.org/abs/2306.14687
The generation of virtual populations (VPs) of anatomy is essential for conducting in silico trials of medical devices. Typically, the generated VP should capture sufficient variability while remaining plausible and should reflect the specific charac
Externí odkaz:
http://arxiv.org/abs/2306.14680
Autor:
Huang, Yuhao, Yang, Xin, Huang, Xiaoqiong, Zhou, Xinrui, Chi, Haozhe, Dou, Haoran, Hu, Xindi, Wang, Jian, Deng, Xuedong, Ni, Dong
Deep classifiers may encounter significant performance degradation when processing unseen testing data from varying centers, vendors, and protocols. Ensuring the robustness of deep models against these domain shifts is crucial for their widespread cl
Externí odkaz:
http://arxiv.org/abs/2306.02544