Results 231 to 240 of about 1,138,724 (269)

Leveraging Co-Occurrence to Improve Deep Learning Photo-Identification in Social Animals. [PDF]

Ecol Evol
Barnhill A, Towers JR, Sutton GJ, Shaw TJH, Doniol-Valcroze T, Maier A, Nöth E, Bergler C.   +7 more
europepmc   +1 more source

Deep Guided Exposure Correction with Knowledge Distillation. [PDF]

Sensors (Basel)
Liu S, Zhang T.
europepmc   +1 more source

A deep-learning-based pipeline for automatic fusion of CT coronary angiogram and stress perfusion CMR. [PDF]

Med Phys
Jiang W, Ng MY, Sin TH, Cao P.
europepmc   +1 more source

Accuracy of Deep Learning for Detecting Axillary Lymph Node Metastasis in Breast Cancer: Systematic Review and Meta-Analysis.

J Med Internet Res
Wang X, Li T, Wang X, Fu D.
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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Deep Gaussian Scale Mixture Prior for Image Reconstruction

IEEE Transactions on Pattern Analysis and Machine Intelligence, 2023
Image reconstruction from partial observations has attracted increasing attention. Conventional image reconstruction methods with hand-crafted priors often fail to recover fine image details due to the poor representation capability of the hand-crafted priors.
Tao Huang, Xin Yuan, Weisheng Dong, Jinjian Wu, Guangming Shi   +4 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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Image Restoration with Structured Deep Image Prior

2021 36th International Technical Conference on Circuits/Systems, Computers and Communications (ITC-CSCC), 2021
In this study, a novel image restoration method is proposed by introducing a structured convolutional neural network (CNN) in the deep image prior (DIP) framework. CNN has shown significance for image restoration as well as classification. DIP uses CNN structures as an image prior and shows a significant performance without explicit training of the ...
Jikai Li, Ruiki Kobayashi, Shogo Muramatsu, Gwanggil Jeon   +3 more
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