Cross-domain, soft-partition clustering with diversity measure and knowledge reference

Qian, Pengjiang; Sun, Shuifa; Jiang, Yizhang; Su, Kuan-Hao; Ni, Tongguang; Wang, Shitong; Muzic, Raymond F.

doi:10.1016/j.patcog.2015.08.009

Cited by 49 publications

(28 citation statements)

References 65 publications

(92 reference statements)

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“…That is, any nine patients' MR-CT image data were taken as the input to train the network parameters, and the remaining patient's image data were adopted to test the accuracy of our proposed model. For the purpose of performance comparison, in addition to the proposed MCRCGAN, two traditional machine learning methods − FCM [13] and TFCM [35] − and two deep learning methods − VGG19 [36] and ResNet [29] − were employed.…”

Section: Experiments a Setupmentioning

confidence: 99%

Multichannel Residual Conditional GAN-Leveraged Abdominal Pseudo-CT Generation via Dixon MR Images

Cao

Xia

et al. 2019

IEEE Access

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Magnetic resonance (MR) images have distinctive advantages in radiation treatment (RT) planning due to their superior, anatomic and functional information compared with computed tomography (CT). For the RT dose calculation, MR images cannot be directly used because of the lack of electron density information. To address this issue, we propose to generate pseudo-CT (pCT) in terms of multiple matching Dixon MR images to support MR-only RT, particularly in the challenging body section of the abdomen. To this end, we design the dedicated multichannel residual conditional generative adversarial network (MCRCGAN). The significance of our efforts is threefold: 1) The MCRCGAN organically incorporates multiple theories and techniques, such as multichannel residual network (ResNet) and conditional generative adversarial network (cGAN), which facilitate its more authentic pCT generation than many existing methods. 2) The usage of residual modules effectively deepens the network without performance degradation, and the multichannel ResNet helps to simultaneously capture the substance of images, as extensively as possible, which is implicitly contained in the multiple different MR images of the same subject. 3) Due to the designed dedicated network structure, the MCRCGAN is capable of generating satisfactory pCTs under the condition of limited training data as well as prompt prediction response. Experimental studies on ten patients' paired MR-CT images demonstrate the effectiveness of our proposed MCRCGAN model on both the pCT generation quality and the performance stability. INDEX TERMS Generative adversarial network (GAN), pseudo-CT, abdomen, deep learning.

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Section: Experiments a Setupmentioning

confidence: 99%

Multichannel Residual Conditional GAN-Leveraged Abdominal Pseudo-CT Generation via Dixon MR Images

Cao

Xia

et al. 2019

IEEE Access

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“…Privacy budget allocation: In this paper, the total privacy budget value ε/k is divided into three parts ε C , ε E , ε R , which are used in the three processes of the algorithm. ε E is divided into two parts: ε cnt is used to calculate the number of value 1 in the noised region [28,29]; ε Par is used to select the segmentation points. Generally speaking, in deciding the allocation of budget value, more budget value is allocated to ε cnt and ε Par , because only after obtaining a relatively accurate number of value 1 after adding noise can it more accurately reconstructs the adjacency matrix to be published [30].…”

Section: Explore Dense Regionmentioning

confidence: 99%

Dynamic social privacy protection based on graph mode partition in complex social network

Qilian

Meng

et al. 2019

Pers Ubiquit Comput

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Differential privacy protection model provides strict and quantitative risk representation for privacy disclosure, which greatly ensures the availability of data. However, most existing methods do not consider the semantic context, so they are vulnerable to attacks based on semantic information. Therefore, dynamic social privacy protection based on graph pattern partitioning is designed to satisfy differential privacy protection. Firstly, the structure of social network is represented as a graph model, and the original graph is classified into several sub-graphs according to the characteristics of nodes. Then, the dense area of each subgraph is divided by quad-tree method, and the noise of differential privacy protection is added to the leaf nodes of the tree, and the graph publishing is generated by sub-graph reconstruction. Finally, the feasibility and practicability of the model are verified by statistical analysis, such as degree distribution, shortest path, and clustering coefficient. The simulation results show the validity and applicability of the privacy protection method proposed in this paper.

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“…It often occurs once a semester and is not rigorous. In summary, this research proposes an automatic anxiety recognition method based on deep features and machine learning [19][20][21][22][23][24][25][26][27]. The main work of this paper is summarized as follows.…”

Section: Introductionmentioning

confidence: 99%

Anxiety Recognition of College Students Using a Takagi-Sugeno-Kang Fuzzy System Modeling Method and Deep Features

Meng

Zhang

2020

IEEE Access

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College students are the most active, most sensitive, and most prone group with respect to various psychological problems in contemporary society. In recent years, with the intensification of social competition, including various pressures such as studies, examinations, economic loss, emotional loss, and employment, the incidence of anxiety, depression and suicide rates has increased. To effectively pay attention to the psychological development of college students and to strengthen mental health education, this research proposes a method to automatically identify the anxiety of college students using a Takagi-Sugeno-Kang (TSK) fuzzy system and deep features. First, preprocess the collected EEG of college students. Secondly, use convolutional neural network (CNN) to extract deep features from the input data. Finally, TSK fuzzy system is used to classify features to obtain the final recognition result. Through experiments on standard data sets and self-made data sets, the experimental results verify the superiority of the anxiety identification method used in this study. The experimental results further demonstrate that the depth features have richer information than traditional features. The noise immunity of TSK fuzzy system makes it show good classification performance and generalization. The recognition results can quickly locate students with anxiety disorders and narrow the scope of investigation for students with psychological problems. The automatic recognition of college students' anxiety can improve the efficiency of schools and teachers in investigating students' psychological problems. This research has very good practical application value.

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Cross-domain, soft-partition clustering with diversity measure and knowledge reference

Cited by 49 publications

References 65 publications

Multichannel Residual Conditional GAN-Leveraged Abdominal Pseudo-CT Generation via Dixon MR Images

Multichannel Residual Conditional GAN-Leveraged Abdominal Pseudo-CT Generation via Dixon MR Images

Dynamic social privacy protection based on graph mode partition in complex social network

Anxiety Recognition of College Students Using a Takagi-Sugeno-Kang Fuzzy System Modeling Method and Deep Features

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