Zobrazeno 1 - 10
of 187
pro vyhledávání: '"Ye, Chuyang"'
Autor:
Ye, Chuyang, Wei, Dongyan, Liu, Zhendong, Pang, Yuanyi, Lin, Yixi, Liao, Jiarong, Jiang, Qinting, Fu, Xianghua, Li, Qing, Jiang, Jingyan
Test-time adaptation (TTA) effectively addresses distribution shifts between training and testing data by adjusting models on test samples, which is crucial for improving model inference in real-world applications. However, traditional TTA methods ty
Externí odkaz:
http://arxiv.org/abs/2408.08056
Contrast-enhanced magnetic resonance imaging (MRI) is pivotal in the pipeline of brain tumor segmentation and analysis. Gadolinium-based contrast agents, as the most commonly used contrast agents, are expensive and may have potential side effects, an
Externí odkaz:
http://arxiv.org/abs/2406.16074
Despite progress, deep neural networks still suffer performance declines under distribution shifts between training and test domains, leading to a substantial decrease in Quality of Experience (QoE) for multimedia applications. Existing test-time ada
Externí odkaz:
http://arxiv.org/abs/2406.05413
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:
Huijben, Evi M. C., Terpstra, Maarten L., Galapon, Arthur Jr., Pai, Suraj, Thummerer, Adrian, Koopmans, Peter, Afonso, Manya, van Eijnatten, Maureen, Gurney-Champion, Oliver, Chen, Zeli, Zhang, Yiwen, Zheng, Kaiyi, Li, Chuanpu, Pang, Haowen, Ye, Chuyang, Wang, Runqi, Song, Tao, Fan, Fuxin, Qiu, Jingna, Huang, Yixing, Ha, Juhyung, Park, Jong Sung, Alain-Beaudoin, Alexandra, Bériault, Silvain, Yu, Pengxin, Guo, Hongbin, Huang, Zhanyao, Li, Gengwan, Zhang, Xueru, Fan, Yubo, Liu, Han, Xin, Bowen, Nicolson, Aaron, Zhong, Lujia, Deng, Zhiwei, Müller-Franzes, Gustav, Khader, Firas, Li, Xia, Zhang, Ye, Hémon, Cédric, Boussot, Valentin, Zhang, Zhihao, Wang, Long, Bai, Lu, Wang, Shaobin, Mus, Derk, Kooiman, Bram, Sargeant, Chelsea A. H., Henderson, Edward G. A., Kondo, Satoshi, Kasai, Satoshi, Karimzadeh, Reza, Ibragimov, Bulat, Helfer, Thomas, Dafflon, Jessica, Chen, Zijie, Wang, Enpei, Perko, Zoltan, Maspero, Matteo
Radiation therapy plays a crucial role in cancer treatment, necessitating precise delivery of radiation to tumors while sparing healthy tissues over multiple days. Computed tomography (CT) is integral for treatment planning, offering electron density
Externí odkaz:
http://arxiv.org/abs/2403.08447
Synthesizing healthy brain scans from diseased brain scans offers a potential solution to address the limitations of general-purpose algorithms, such as tissue segmentation and brain extraction algorithms, which may not effectively handle diseased im
Externí odkaz:
http://arxiv.org/abs/2402.01509
Autor:
Liu, Wan, Ye, Chuyang
White matter (WM) tract segmentation is a crucial step for brain connectivity studies. It is performed on diffusion magnetic resonance imaging (dMRI), and deep neural networks (DNNs) have achieved promising segmentation accuracy. Existing DNN-based m
Externí odkaz:
http://arxiv.org/abs/2309.13980
Publikováno v:
Jisuanji kexue yu tansuo, Vol 18, Iss 9, Pp 2337-2348 (2024)
In the Chinese radiology domain, radiology reports serve as a crucial basis for clinical decision-making. Therefore, utilizing natural language processing (NLP) technology to understand and learn from the textual content of radiology reports, thereby
Externí odkaz:
https://doaj.org/article/f5fe367195184216a4b02d57101a21cf
Automated brain tumor segmentation based on deep learning (DL) has achieved promising performance. However, it generally relies on annotated images for model training, which is not always feasible in clinical settings. Therefore, the development of u
Externí odkaz:
http://arxiv.org/abs/2304.01472
Autor:
Liu, Wan, Chen, Yuqian, Ye, Chuyang, Makris, Nikos, Rathi, Yogesh, Cai, Weidong, Zhang, Fan, O'Donnell, Lauren J.
Neuroimaging measures of the brain's white matter connections can enable the prediction of non-imaging phenotypes, such as demographic and cognitive measures. Existing works have investigated traditional microstructure and connectivity measures from
Externí odkaz:
http://arxiv.org/abs/2303.09124