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您的检索式：作者名="Kaiyi Liang"
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| 1 | Inner Cascaded U^(2)-Net:An Improvement to Plain Cascaded U-Net显示文摘Deep neural networks are now widely used in the medical image segmentation field for their performance superiority and no need of manual feature extraction.U-Net has been the baseline model since the very beginning due to a symmetricalU-structure for better feature extraction and fusing and suitable for small datasets.To enhance the segmentation performance of U-Net,cascaded U-Net proposes to put two U-Nets successively to segment targets from coarse to fine.However,the plain cascaded U-Net faces the problem of too less between connections so the contextual information learned by the former U-Net cannot be fully used by the latter one.In this article,we devise novel Inner Cascaded U-Net and Inner Cascaded U^(2)-Net as improvements to plain cascaded U-Net for medical image segmentation.The proposed Inner Cascaded U-Net adds inner nested connections between two U-Nets to share more contextual information.To further boost segmentation performance,we propose Inner Cascaded U^(2)-Net,which applies residual U-block to capture more global contextual information from different scales.The proposed models can be trained from scratch in an end-to-end fashion and have been evaluated on Multimodal Brain Tumor Segmentation Challenge(BraTS)2013 and ISBI Liver Tumor Segmentation Challenge(LiTS)dataset in comparison to related U-Net,cascaded U-Net,U-Net++,U^(2)-Net and state-of-the-art methods.Our experiments demonstrate that our proposed Inner Cascaded U-Net and Inner Cascaded U^(2)-Net achieve better segmentation performance in terms of dice similarity coefficient and hausdorff distance as well as get finer outline segmentation. | Wenbin Wu Guanjun Liu Kaiyi Liang Hui Zhou | 2023 | Computer Modeling in Engineering & Sciences2023，，2： | 0 |
| 2 | Angle-uniform parallel coordinates显示文摘We present angle-uniform parallel coordinates,a data-independent technique that deforms the image plane of parallel coordinates so that the angles of linear relationships between two variables are linearly mapped along the horizontal axis of the parallel coordinates plot.Despite being a common method for visualizing multidimensional data,parallel coordinates are ineffective for revealing positive correlations since the associated parallel coordinates points of such structures may be located at infinity in the image plane and the asymmetric encoding of negative and positive correlations may lead to unreliable estimations.To address this issue,we introduce a transformation that bounds all points horizontally using an angleuniform mapping and shrinks them vertically in a structure-preserving fashion;polygonal lines become smooth curves and a symmetric representation of data correlations is achieved.We further propose a combined subsampling and density visualization approach to reduce visual clutter caused by overdrawing.Our method enables accurate visual pattern interpretation of data correlations,and its data-independent nature makes it applicable to all multidimensional datasets.The usefulness of our method is demonstrated using examples of synthetic and real-world datasets. | Kaiyi Zhang Liang Zhou Lu Chen Shitong He Daniel Weiskopf Yunhai Wang | 2023 | Computational Visual Media2023，9，3： | 0 |
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