检索结果-内蒙古大学图书馆

5th International Workshop on graphs in Biomedical Image Analysis (GRAIL) / Workshop on Overlapped Celi on Tissue - Cell Detection from Cell-Tissue Interaction Challenge (OCELOT) / 5th MICCAI Workshop / 1st MICCAI Challenge Workshop

作者： Madeira, Manuel Thanou, Dorina Frossard, Pascal Ecole Polytech Fed Lausanne Lausanne Switzerland

ISBN: (纸本)9783031550874;9783031550881

graph-based representation approaches have been proven to be successful in the analysis of biomedical data due to their capability of capturing intricate dependencies between biological entities, such as the spatial organization of different cell types in a tumor tissue. However, to further enhance our understanding of the underlying governing biological mechanisms, it is important to accurately capture the actual distributions of such complex data. graph-based deep generative models are specifically tailored to accomplish that. In this work, we leverage state-of-the-art graph-based diffusion models to generate biologically meaningful cell-graphs. In particular, we show that the adopted graph diffusion model is able to accurately learn the distribution of cells in terms of their tertiary lymphoid structures (TLS) content, a well-established biomarker for evaluating the cancer progression in oncology research. Additionally, we further illustrate the utility of the learned generative models for data augmentation in a TLS classification task. To the best of our knowledge, this is the first work that leverages the power of graph diffusion models in generating meaningful biological cell structures.

关键词： Deep generative models graph-based diffusion Tertiary lymphoid structures

来源：评论

学校读者我要写书评

暂无评论

Intensifying graph diffusion-based salient object detection with sparse graph weighting

引用

MULTIMEDIA TOOLS AND APPLICATIONS 2023年第22期82卷 34113-34127页

作者： Wang, Fan Peng, Guohua Xian Shiyou Univ Sch Sci Xian 710065 Shaanxi Peoples R China Northwestern Polytecn Univ Sch Math & Stat Xian 710129 Shaanxi Peoples R China

Salient object detection based on the diffusion process on the graph has achieved considerable performance. It mainly depends on the affinity matrix construction considering the local structure. This paper aims to depict the local and global structures from image features, intensifying the graph-based diffusion model by simultaneously integrating the sparse graph matrix and affinity graph matrix. The contribution work computes the affinity graph matrix and delivers an affinity matrix by incorporating the sparse representation and diffusion process. It estimates a sparse graph matrix by integrating sparse representation and laplacian smoothness. To this end, a two-stage graph-based diffusion model has been constructed by embedding the manifold smoothness and manifold reconstruction. The first stage follows the boundary-prior to generate a coarse saliency map. After, the second stage combines the saliency map and Harris convex hull to obtain the foreground seeds. Extensive experiments on six benchmark datasets have demonstrated the superiority of the proposed method compared to other state-of-the-art methods.

关键词： Salient object detection graph-based diffusion Sparse graph matrix Affinity graph matrix Local and global structure

来源：评论

学校读者我要写书评

暂无评论

Robust manifold-preserving diffusion-based saliency detection by adaptive weight construction

引用

NEUROCOMPUTING 2016年第PARTA期175卷 336-347页

作者： Fu, Keren Gu, Irene Y. H. Gong, Chen Yang, Jie Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai 200030 Peoples R China Chalmers Univ Technol Dept Signals & Syst Signal Proc Grp S-41296 Gothenburg Sweden

graph-based diffusion techniques have drawn Much interest lately for salient object detection. The diffusion performance is heavily dependent on the edge weights in graph representing the similarity between nodes, and are usually set through manually tuning. To improve the diffusion performance, this paper proposes a robust diffusion scheme, referred to as manifold-preserving diffusion (MPD), that is built jointly on two assumptions for preserving the manifold used in saliency detection. The smoothness assumption reflects the conditional random field (CRF) property and the related penalty term enforces similar saliency on similar graph neighbors. The penalty term related to the local reconstruction assumption enforces a local linear mapping from the feature space to saliency values. graph edge weights in the above two penalties in the proposed MPD method are determined adaptively by minimizing local reconstruction errors in feature space. This enables a better adaption of diffusion on different images. The final diffusion process is then formulated as a regularized optimization problem, taking into account of initial seeds, manifold smoothness and local reconstruction. Consequently, when applied to saliency diffusion, MPD provides a higher performance upper bound than some existing diffusion methods such as manifold ranking. By utilizing MPD, we further introduce a two-stage saliency detection scheme, referred to as manifold-preserving diffusion-based saliency (MPDS), where boundary prior, Harris convex hull, and foci convex hull are employed for deriving initial seeds and a coarse map for MPD. Experiments were conducted on five benchmark datasets and compared with eight existing methods. Our results show that the proposed method is robust in terms of consistently achieving the highest weighted F-measure and lowest mean absolute error, meanwhile maintaining comparable precision-recall curves. Salient objects in different background can be uniformly highlighted in th

关键词： Salient object detection Saliency map Manifold graph-based diffusion

来源：评论

学校读者我要写书评

暂无评论

LEARNING FULL-RANGE AFFINITY FOR diffusion-based SALIENCY DETECTION 41

LEARNING FULL-RANGE AFFINITY FOR DIFFUSION-BASED SALIENCY DE...

引用

41st IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP)

作者： Fu, Keren Gu, Irene Y. H. Yang, Jie Chalmers Univ Technol Dept Signals & Syst Gothenburg Sweden Shanghai Jiao Tong Univ Inst Image Proc & Pattern Recognit Shanghai Peoples R China

ISBN: (纸本)9781479999880

In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperforms 7 recent state-of-the-art saliency models on 3 benchmark datasets.

关键词： Saliency detection graph-based diffusion affinity learning semi-supervised learning

来源：评论

学校读者我要写书评

暂无评论

LEARNING FULL-RANGE AFFINITY FOR diffusion-based SALIENCY DETECTION

LEARNING FULL-RANGE AFFINITY FOR DIFFUSION-BASED SALIENCY DE...

引用

IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Keren Fu Irene Y.H. Gu Jie Yang Department of Signals and Systems Chalmers University of Technology Gothenburg Sweden Institute of Image Processing and Pattern Recognition Shanghai Jiao Tong University China

ISBN: (纸本)9781479999897

In this paper we address the issue of enhancing salient object detection through diffusion-based techniques. For reliably diffusing the energy from labeled seeds, we propose a novel graph-based diffusion scheme called affinity learning-based diffusion (ALD), which is based on learning full-range affinity between two arbitrary graph nodes. The method differs from the previous existing work where implicit diffusion was formulated as a ranking problem on a graph. In the proposed method, the affinity learning is achieved in a unified graph-based semi-supervised manner, whose outcome is leveraged for global propagation. By properly selecting an affinity learning model, the proposed ALD outperforms the ranking-based diffusion in terms of accurately detecting salient objects and enhancing the correct salient objects under a range of background scenarios. By utilizing the ALD, we propose an enhanced saliency detector that outperfomis 7 recent state-of-the-art saliency models on 3 benchmark datasets.

关键词： Saliency detection graph-based diffusion affinity learning semi-supervised learning Semi-supervised Learning affinity diffusion Adrenoleukodystrophy Affine transformation Object detection Learning

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

时间限定

文献类型

馆藏选择

核心期刊

语言

文献类型

帮助

文字说明：

检索规则说明：

检索范例：

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：