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Yuanyuan Jia
Researcher at Chongqing Medical University
Publications - 20
Citations - 340
Yuanyuan Jia is an academic researcher from Chongqing Medical University. The author has contributed to research in topics: Convolutional neural network & Feature (computer vision). The author has an hindex of 7, co-authored 17 publications receiving 172 citations. Previous affiliations of Yuanyuan Jia include Chongqing University.
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AFPNet: A 3D fully convolutional neural network with atrous-convolution feature pyramid for brain tumor segmentation via MRI images
TL;DR: A 3D atrous-convolution with a single stride to replace pooling/striding and build the backbone for feature learning and a 3D fully connected Conditional Random Field is constructed as a post-processing step for the network's output to obtain structural segmentation of both the appearance and spatial consistency.
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Super-resolution reconstruction of single anisotropic 3D MR images using residual convolutional neural network
TL;DR: Experimental results show that the proposed CNN-based anisotropic MR image reconstruction method outperforms classical interpolation methods, non-local means method (NLM), and sparse coding based algorithm in terms of peak signal-to-noise-ratio, structural similarity image index, intensity profile, and small structures.
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A New Sparse Representation Framework for Reconstruction of an Isotropic High Spatial Resolution MR Volume From Orthogonal Anisotropic Resolution Scans
TL;DR: This work proposes a multiframe super-resolution reconstruction technique based on sparse representation of MR images that can reduce through-plane partial volume artifact by combining multiple orthogonal MR scans, and thus can potentially improve medical image analysis, research, and clinical diagnosis.
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A Novel Approach to Multiple Sequence Alignment Using Multiobjective Evolutionary Algorithm Based on Decomposition
TL;DR: Experimental results show that MOMSA can obtain the significantly better alignments than VDGA, GAPAM on the most of test cases by statistical analyses, produce better alignings than IMSA in terms of TC scores, and also indicate that MMomSA is comparable with the leading progressive alignment approaches in termsof quality of alignments.
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Single Anisotropic 3-D MR Image Upsampling via Overcomplete Dictionary Trained From In-Plane High Resolution Slices
TL;DR: A novel single anisotropic 3-D MR image upsampling method via sparse representation and overcomplete dictionary that is trained from in-plane high resolution slices to upsample in the out-of-plane dimensions that is more accurate than classical interpolation and does not require extra training sets.