Zobrazeno 1 - 10
of 14
pro vyhledávání: '"Yunzhe Xue"'
Autor:
Yunzhe Xue, Fadi G. Farhat, Olga Boukrina, A.M. Barrett, Jeffrey R. Binder, Usman W. Roshan, William W. Graves
Publikováno v:
NeuroImage: Clinical, Vol 25, Iss , Pp - (2020)
Automatic identification of brain lesions from magnetic resonance imaging (MRI) scans of stroke survivors would be a useful aid in patient diagnosis and treatment planning. It would also greatly facilitate the study of brain-behavior relationships by
Externí odkaz:
https://doaj.org/article/cb5a034186f348dc8d8d64506d378d9b
Publikováno v:
BIBM
Prediction of cancer survival time is of considerable interest in medicine as it leads to better patient care and reduces health care costs. In this study, we propose a multi-path multimodal neural network that predicts Glioblastoma Multiforme (GBM)
Autor:
Yunzhi Li, Yunzhe Xue, Saum Rahimi, Lauren A. Huntress, Usman Roshan, William E. Beckerman, Meiyan Xie, Justin Ady
Publikováno v:
BIBM
Carotid ultrasound is a screening modality used by physicians to direct treatment in the prevention of ischemic stroke in high-risk patients. It is a time intensive process that requires highly trained technicians and physicians. Evaluation of a caro
Publikováno v:
BIBM
Adversarial attacks in medical AI imaging systems can lead to misdiagnosis and insurance fraud as recently highlighted by Finlayson et. al. in Science 2019. They can also be carried out on widely used ECG time-series data as shown in Han et. al. in N
Publikováno v:
ICMLA
Motivated by the general robustness properties of the 01 loss we propose a single hidden layer 01 loss neural network trained with stochastic coordinate descent as a defense against adversarial attacks in machine learning. One measure of a model's ro
Autor:
William E. Beckerman, Justin Ady, Meiyan Xie, Yunzhu Li, Saum Rahimi, Usman Roshan, Lauren A. Huntress, Yunzhe Xue
Publikováno v:
ICMLA
Carotid ultrasound is a screening modality used by physicians to direct treatment in the prevention of ischemic stroke in high-risk patients. It is a time intensive process that requires highly trained technicians and physicians. Evaluation of a caro
Publikováno v:
ICMLA
The 01 loss gives different and more accurate boundaries than convex loss models in the presence of outliers. Could the difference of boundaries translate to adversarial examples that are non-transferable between 01 loss and convex models? We explore
Publikováno v:
2020 7th International Conference on Bioinformatics Research and Applications.
The classification of whole slide images plays an important role in understanding and diagnosing cancer. Pathologists typically have to work through numerous pathology images that can be in the order of hundreds or thousands which takes time and is p
Autor:
Anna M. Barrett, Yunzhe Xue, Usman Roshan, Fadi G. Farhat, Jeffrey R. Binder, William W. Graves, Olga Boukrina, Meiyan Xie
Publikováno v:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030466428
BrainLes@MICCAI (2)
BrainLes@MICCAI (2)
The identification of brain tumor type, shape, and size from MRI images plays an important role in glioma diagnosis and treatment. Manually identifying the tumor is time expensive and prone to error. And while information from different image modalit
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::afbaaac2b3304ef2ba0a8c5781d1e3cc
https://doi.org/10.1007/978-3-030-46643-5_25
https://doi.org/10.1007/978-3-030-46643-5_25
Autor:
Yanan Yang, Usman Roshan, William W. Graves, Frank Y. Shih, Yunzhe Xue, Fadi G. Farhat, Jeffrey R. Binder, Anna M. Barrett, Olga Boukrina
Publikováno v:
Brainlesion: Glioma, Multiple Sclerosis, Stroke and Traumatic Brain Injuries ISBN: 9783030466428
BrainLes@MICCAI (2)
BrainLes@MICCAI (2)
Brain tumor classification plays an important role in brain cancer diagnosis and treatment. Pathologists typically have to work through numerous pathology images that can be in the order of hundreds or thousands which takes time and is prone to manua
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::7a1a3f6f8f3aa710da15ce78cf48b658
https://doi.org/10.1007/978-3-030-46643-5_36
https://doi.org/10.1007/978-3-030-46643-5_36