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arXiv 2021年

作者： Xia, Shuyin Bai, Xinyu Wang, Guoyin Meng, Deyu Gao, Xinbo Chen, Zizhong Giem, Elisabeth The Chongqing Key Laboratory of Computational Intelligence Chongqing University of Telecommunications and Posts Chongqing 400065 China The National Engineering Laboratory for Algorithm and Analysis Technologiy on Big Data Xi'an Jiaotong University Xi'An710049 China The Department of Computer Science and Engineering University of California Riverside RiversideCA92521 United States

This paper present a strong data mining method based on rough set, which can realize feature selection, classification and knowledge representation at the same time. Rough set has good interpretability, and is a popular method for feature selections. But low efficiency and low accuracy are its main drawbacks that limits its application ability. In this paper,corresponding to the accuracy, we first find the ineffectiveness of rough set because of overfitting, especially in processing noise attribute, and propose a robust measurement for an attribute, called relative *** proposed the concept of "rough concept tree" for knowledge representation and classification. Experimental results on public benchmark data sets show that the proposed framework achieves higher accurcy than seven popular or the state-of-the-art feature selection methods. Copyright © 2021, The Authors. All rights reserved.

关键词： Feature extraction

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Neural multi-atlas label fusion: Application to cardiac MR images

arXiv

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arXiv 2017年

作者： Yang, Heran Sun, Jian Li, Huibin Wang, Lisheng Xu, Zongben School of Mathematics and Statistics Xi’an Jiaotong University China National Engineering Laboratory for Big Data Algorithm and Analysis Technology China Department of Automation Shanghai Jiaotong University China

Multi-atlas segmentation approach is one of the most widely-used image segmentation techniques in biomedical applications. There are two major challenges in this category of methods, i.e., atlas selection and label fusion. In this paper, we propose a novel multi-atlas segmentation method that formulates multi-atlas segmentation in a deep learning framework for better solving these challenges. The proposed method, dubbed deep fusion net (DFN), is a deep architecture that integrates a feature extraction subnet and a non-local patch-based label fusion (NL-PLF) subnet in a single network. The network parameters are learned by end-to-end training for automatically learning deep features that enable optimal performance in a NL-PLF framework. The learned deep features are further utilized in defining a similarity measure for atlas selection. By evaluating on two public cardiac MR datasets of SATA-13 and LV-09 for left ventricle segmentation, our approach achieved 0.833 in averaged Dice metric (ADM) on SATA-13 dataset and 0.95 in ADM for epicardium segmentation on LV-09 dataset, comparing favorably with the other automatic left ventricle segmentation methods. We also tested our approach on Cardiac Atlas Project (CAP) testing set of MICCAI 2013 SATA Segmentation Challenge, and our method achieved 0.815 in ADM, ranking highest at the time of writing. Copyright © 2017, The Authors. All rights reserved.

关键词： Image segmentation

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Ball k-means

arXiv

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arXiv 2020年

作者： Xia, Shuyin Peng, Daowan Meng, Deyu Zhang, Changqing Wang, Guoyin Chen, Zizhong Wei, Wei Department of Chongqing Key Laboratory of Computational Intelligence Chongqing University of Posts and Telecommunications Chongqing400065 China National Engineering Laboratory for Algorithm and Analysis Technologiy on Big Data Xian Jiaotong University Xi'an710049 China College of Intelligence and Computing Tianjin University 300072 China Department of Computer Science and Engineering University of California Riverside 900 University Avenue RiversideCA92521 United States School of Computer Science and Engineering Xi'an University of Technology Xi'an710048 China

This paper presents a novel accelerated exact k-means algorithm called the Ball k-means algorithm, which uses a ball to describe a cluster, focusing on reducing the point-centroid distance computation. The Ball k-means can accurately find the neighbor clusters for each cluster resulting distance computations only between a point and its neighbor clusters centroids instead of all centroids. Moreover, each cluster can be divided into a stable area and an active area, and the later one can be further divided into annulus areas. The assigned cluster of the points in the stable area is not changed in the current iteration while the points in the annulus area will be adjusted within a few neighbor clusters in the current iteration. Also, there are no upper or lower bounds in the proposed Ball k-means. Furthermore, reducing centroid-centroid distance computation between iterations makes it efficient for large k clustering. The fast speed, no extra parameters and simple design of the Ball k-means make it an all-around replacement of the naive k-means algorithm. Copyright © 2020, The Authors. All rights reserved.

关键词： K-means clustering

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Medical Image Segmentation Using Deep Learning: A Survey

arXiv

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arXiv 2020年

作者： Wang, Risheng Lei, Tao Cui, Ruixia Zhang, Bingtao Meng, Hongying Nandi, Asoke K. The School of Electronic Information and Artificial Intelligence The Shaanxi Joint Laboratory of Artificial Intelligence Shaanxi University of Science and Technology Xi'An710021 China The 'Laboratory of Hepatobiliary Surgery First Affiliated Hospital' 'National Engineering Laboratory of Big Data Algorithm and Analysis Technology Research' Xi'an Jiaotong University Xi'An710049 China The School of Electronic and Information Engineering Lanzhou Jiaotong University Lanzhou730070 China The Department of Electronic and Electrical Engineering Brunel University London UxbridgeUB8 3PH United Kingdom

Deep learning has been widely used for medical image segmentation and a large number of papers has been presented recording the success of deep learning in the field. In this paper, we present a comprehensive thematic survey on medical image segmentation using deep learning techniques. This paper makes two original contributions. Firstly, compared to traditional surveys that directly divide literatures of deep learning on medical image segmentation into many groups and introduce literatures in detail for each group, we classify currently popular literatures according to a multi-level structure from coarse to fine. Secondly, this paper focuses on supervised and weakly supervised learning approaches, without including unsupervised approaches since they have been introduced in many old surveys and they are not popular currently. For supervised learning approaches, we analyze literatures in three aspects: the selection of backbone networks, the design of network blocks, and the improvement of loss functions. For weakly supervised learning approaches, we investigate literature according to data augmentation, transfer learning, and interactive segmentation, separately. Compared to existing surveys, this survey classifies the literatures very differently from before and is more convenient for readers to understand the relevant rationale and will guide them to think of appropriate improvements in medical image segmentation based on deep learning approaches. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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