Zobrazeno 1 - 4
of 4
pro vyhledávání: '"Xiaoyue Mi"'
Autor:
Chee Teik Lee, Ana S. Salazar, Adetunji T. Toriola, Xiaoyue Mi, Shuai Xu, Malika Rakhmankulova, Yunan Han, Courtnie R. Phillip
Publikováno v:
Breast Cancer Res Treat
PURPOSE: A dense breast on mammogram is a strong risk factor for breast cancer. Identifying factors that reduce mammographic breast density could thus provide insight into breast cancer prevention. Due to the limited number of studies and conflicting
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::34e6c0a41f6bceef0e92ff37a9cc7ba4
https://doi.org/10.1007/s10549-021-06321-5
https://doi.org/10.1007/s10549-021-06321-5
Publikováno v:
Cancer Research. 80:P5-08
Background: Mammographic breast density is one of the strongest risk factors for breast cancer. Hence, identifying factors that can reduce mammographic breast density could have utility in breast cancer prevention. A few studies have evaluated the as
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::44438b8a7fd73f3fe8d14ad640b497bc
https://doi.org/10.1158/1538-7445.sabcs19-p5-08-17
https://doi.org/10.1158/1538-7445.sabcs19-p5-08-17
Improving Fake News Detection by Using an Entity-enhanced Framework to Fuse Diverse Multimodal Clues
Autor:
Qiang Sheng, Yongbiao Lv, Peng Qi, Huan Liu, Qin He, Xirong Li, Chenyang Guo, Yingchao Yu, Juan Cao, Xiaoyue Mi
Publikováno v:
ACM Multimedia
Recently, fake news with text and images have achieved more effective diffusion than text-only fake news, raising a severe issue of multimodal fake news detection. Current studies on this issue have made significant contributions to developing multim
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::5bdceaa84e65856fd9d706f417429cd7
Autor:
Ziyao Huang, Yepeng Weng, Xiaoyue Mi, Ke Gao, Xiaoya Li, Zhengze Yu, Juan Cao, Boyang xia, Lei Li
Publikováno v:
CVPR
Single domain generalization is a challenging case of model generalization, where the models are trained on a single domain and tested on other unseen domains. A promising solution is to learn cross-domain invariant representations by expanding the c
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83efbfda82372c5f68619f871d3caa9b