Zobrazeno 1 - 10
of 136
pro vyhledávání: '"Wang Chengjia"'
We address two fundamental challenges in Graph Neural Networks (GNNs): (1) the lack of theoretical support for invariance learning, a critical property in image processing, and (2) the absence of a unified model capable of excelling on both homophili
Externí odkaz:
http://arxiv.org/abs/2411.19392
Graph Neural Networks (GNNs) have advanced relational data analysis but lack invariance learning techniques common in image classification. In node classification with GNNs, it is actually the ego-graph of the center node that is classified. This res
Externí odkaz:
http://arxiv.org/abs/2411.08758
The persistent challenge of medical image synthesis posed by the scarcity of annotated data and the need to synthesize `missing modalities' for multi-modal analysis, underscored the imperative development of effective synthesis methods. Recently, the
Externí odkaz:
http://arxiv.org/abs/2408.07196
Autor:
Wang, Chengjia, Papanastasiou, Giorgos
Clinical decision making from magnetic resonance imaging (MRI) combines complementary information from multiple MRI sequences (defined as 'modalities'). MRI image registration aims to geometrically 'pair' diagnoses from different modalities, time poi
Externí odkaz:
http://arxiv.org/abs/2308.01994
Medical imaging is a key component in clinical diagnosis, treatment planning and clinical trial design, accounting for almost 90% of all healthcare data. CNNs achieved performance gains in medical image analysis (MIA) over the last years. CNNs can ef
Externí odkaz:
http://arxiv.org/abs/2307.12775
Autor:
Xing, Xiaodan, Huang, Jiahao, Nan, Yang, Wu, Yinzhe, Wang, Chengjia, Gao, Zhifan, Walsh, Simon, Yang, Guang
The destitution of image data and corresponding expert annotations limit the training capacities of AI diagnostic models and potentially inhibit their performance. To address such a problem of data and label scarcity, generative models have been deve
Externí odkaz:
http://arxiv.org/abs/2206.13394
Autor:
Morris, David M., Wang, Chengjia, Papanastasiou, Giorgos, Gray, Calum D., Xu, Wei, Sjöström, Samuel, Badr, Sammy, Paccou, Julien, Semple, Scott IK, MacGillivray, Tom, Cawthorn, William P.
Publikováno v:
In Computational and Structural Biotechnology Journal December 2024 24:89-104
Magnetic Resonance Imaging (MRI) typically recruits multiple sequences (defined here as "modalities"). As each modality is designed to offer different anatomical and functional clinical information, there are evident disparities in the imaging conten
Externí odkaz:
http://arxiv.org/abs/2203.03638
Autor:
Semple Scott, Alam Shirjel R, MacGillivray Tom J, Dweck Marc R, Shah Anoop S, Richards Jenny, Wang Chengjia, Lang Ninian, McKillop Graham, Mirsadraee Saeed, Pessotto Renzo, Zamvar Vipin, Henriksen Peter, Newby David
Publikováno v:
Journal of Cardiovascular Magnetic Resonance, Vol 15, Iss Suppl 1, p O114 (2013)
Externí odkaz:
https://doaj.org/article/b08407fd17d145329c46d047c99eabba
Autor:
Ye, Qinghao, Gao, Yuan, Ding, Weiping, Niu, Zhangming, Wang, Chengjia, Jiang, Yinghui, Wang, Minhao, Fang, Evandro Fei, Menpes-Smith, Wade, Xia, Jun, Yang, Guang
The world is currently experiencing an ongoing pandemic of an infectious disease named coronavirus disease 2019 (i.e., COVID-19), which is caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Computed Tomography (CT) plays an i
Externí odkaz:
http://arxiv.org/abs/2112.04984