Results 131 to 140 of about 1,132,388 (176)

Neuropathological correlates of MRI-observed hypointense lesions in the APP23 mouse model of cerebral amyloid angiopathy. [PDF]

J Cereb Blood Flow Metab
van den Berg ML, Munting LP, Hanlin L, Yi I, Bailey J, Richardson DS, Zwang TJ, van Veluw SJ, Kozberg MG.   +8 more
europepmc   +1 more source

Multi-step chestnut physical characteristics classification model based on vision transformation using a single-view RGB image. [PDF]

Sci Rep
Kim TH, Kwon KH, Kim AN.
europepmc   +1 more source

An interpretable cascaded residual iterative network for sparse-view spectral CT imaging. [PDF]

Quant Imaging Med Surg
Zhang X, Wang S, Liang N, Zheng Z, Cai A, Li L, Yu H, Yan B.   +7 more
europepmc   +1 more source

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Medical Image Segmentation With Deep Atlas Prior

IEEE Transactions on Medical Imaging, 2021
Organ segmentation from medical images is one of the most important pre-processing steps in computer-aided diagnosis, but it is a challenging task because of limited annotated data, low-contrast and non-homogenous textures. Compared with natural images, organs in the medical images have obvious anatomical prior knowledge (e.g., organ shape and position)
Huimin Huang, Han Zheng, Lanfen Lin, Ming Cai, Hongjie Hu, Qiaowei Zhang, Qingqing Chen, Yutaro Iwamoto, Xianhua Han, Yen-Wei Chen, Ruofeng Tong   +10 more
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Image demosaicing using Deep Image Prior

Proceedings II of the 29st Conference STUDENT EEICT 2023: Selected papers., 2023
The paper focuses on the problem of image demosaicingusing the deep image prior. The deep image prior (DIP)is an uncommon concept that uses a generative neural networkwhich, however, utilizes only the degraded image as the inputfor training. A novel method for image demosaicing is proposed,based on DIP, and it is compared with common demosaicingmethods.
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EEG Source Imaging using GANs with Deep Image Prior

2022 44th Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC), 2022
Brain source localization from electroencephalogram (EEG) signals is an challenging problem for noninvasively localizing the brain activity. Conventional methods use handcrafted regularization terms based on neural-physiological assumptions by exploiting the spatial-temporal structure on the source signals.
Yaxin, Guo, Meng, Jiao, Guihong, Wan, Jing, Xiang, Shouyi, Wang, Feng, Liu   +5 more
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