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16th International Conference on Ubiquitous Information Management and Communication (IMCOM)

作者： Song, Mingue Kim, Yanggon Towson Univ Dept Comp & Informat Sci Towson MD 21252 USA

ISBN: (纸本)9781665426787

An automated classification of ultrasound breast tumor is a vital step for the early prevention of abnormal breast cells. In general, radiologists manually handle this procedure, but manual analysis performed by individual poses a problem of consistency depending on the experts. One of the standardized alternatives was to apply automated deep learning method in this field. In fact, majority ideas in literature are dominantly based on the supervised learning framework, but even such methods have still failed to present promising discrimination performance. In this work, we assume that unsupervised learning still can be a potential option and beneficial attribute that enables to accelerate discrimination is inherent in it. Hence, we present a deep representation for the ultrasound breast data utilizing two types of independent supervised and unsupervised network to reconstruct the principal features, while the volume of supervised features is set to be minimum and the volume of unsupervised is the maximum. Specifically, we adopted pretrained Resnet34 as a supervised network, and a convolutional autoencoder (CAE) was designed for the unsupervised network. Each representation vector is combined into a single vector, and the generated vector is given to the support vector machine as an input for the final discrimination. The results are verified that the proposed method shows far better performance compared to several conventional deep learning methods and the single use of each method. The value of accuracy, sensitivity and specificity are obtained by 88.18%, 85.25% and 100.00% respectively.

关键词： fusion framework ultrasound breast cancer classification convolutional autoencoder machine-learning

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Deep Clustering for Improved Inter-Cluster Separability and Intra-Cluster Homogeneity with Cohesive Loss

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IEICE TRANSACTIONS ON INFORMATION AND SYSTEMS 2021年第5期E104D卷 776-780页

作者： Kim, Byeonghak Loew, Murray Han, David K. Ko, Hanseok Korea Univ Dept Visual Informat Proc Seoul 02841 South Korea George Washington Univ Dept Biomed Engn Washington DC USA Drexel Univ Dept Elect & Comp Engn Philadelphia PA 19104 USA Korea Univ Sch Elect Engn Seoul 02841 South Korea

To date, many studies have employed clustering for the classification of unlabeled data. Deep separate clustering applies several deep learning models to conventional clustering algorithms to more clearly separate the distribution of the clusters. In this paper, we employ a convolutional autoencoder to learn the features of input images. Following this, k-means clustering is conducted using the encoded layer features learned by the convolutional autoencoder. A center loss function is then added to aggregate the data points into clusters to increase the intra-cluster homogeneity. Finally, we calculate and increase the inter-cluster separability. We combine all loss functions into a single global objective function. Our new deep clustering method surpasses the performance of existing clustering approaches when compared in experiments under the same conditions.

关键词： separate clustering convolutional autoencoder intra-cluster homogeneity inter-cluster separability

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Anomaly detection in genomic catalogues using unsupervised multi-view autoencoders

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BMC BIOINFORMATICS 2021年第1期22卷 1-26页

作者： Ferre, Quentin Cheneby, Jeanne Puthier, Denis Capponi, Cecile Ballester, Benoit Aix Marseille Univ TAGC INSERM Marseille France Aix Marseille Univ Univ Toulon LIS CNRS Marseille France

Background Accurate identification of Transcriptional Regulator binding locations is essential for analysis of genomic regions, including Cis Regulatory Elements. The customary NGS approaches, predominantly ChIP-Seq, can be obscured by data anomalies and biases which are difficult to detect without supervision. Results Here, we develop a method to leverage the usual combinations between many experimental series to mark such atypical peaks. We use deep learning to perform a lossy compression of the genomic regions' representations with multiview convolutions. Using artificial data, we show that our method correctly identifies groups of correlating series and evaluates CRE according to group completeness. It is then applied to the ReMap database's large volume of curated ChIP-seq data. We show that peaks lacking known biological correlators are singled out and less confirmed in real data. We propose normalization approaches useful in interpreting black-box models. Conclusion Our approach detects peaks that are less corroborated than average. It can be extended to other similar problems, and can be interpreted to identify correlation groups. It is implemented in an open-source tool called atyPeak.

关键词： Genomic assay Anomaly detection Cis regulatory element Unsupervised curation convolutional autoencoder ChIP-seq peak quality Model interpretability

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Dual-Y network: infrared-visible image patches matching via semi-supervised transfer learning

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APPLIED INTELLIGENCE 2021年第4期51卷 2188-2197页

作者： Mao, Yuanhong He, Zhanzhuang Xian Microelect Technol Inst Xian 710065 Peoples R China

Infrared-visible image patches matching has many applications, such as target recognition, vision-based navigation, and others. At present, deep learning has achieved excellent performance in visible image patches matching. Due to imaging differences in infrared-visible images and the small number of supervised samples, the existing networks cannot solve them well. We propose a Dual-Y network based on semi-supervised transfer learning to address the challenges. Since the infrared and visible image patches have similarities in the same scene, adversarial domain adaptation uses visible images as the source domain and infrared images as the target domain to solve different imaging principles. Through a large number of unannotated samples training, the two convolutional autoencoders in our network can reconstruct the infrared-visible image patches respectively to improve feature representation ability. In the high-level convolution layers, the cross-domain features are extracted by sharing weights in two domains. The adversarial domain adaptation achieves the domain confusion in the high layers. In the low-level convolutional layers, independent weights in two branches can ensure that the domain-related imaging principles can be preserved. Finally, the annotated data are used for supervised training in infrared-visible image patches matching. With the improvements above, the matching accuracy increase by 10.78%, compared with the fine-tuning based on the pre-training module.

关键词： Infrared-visible image patches matching Adversarial domain adaptation convolutional autoencoder Semi-supervised transfer learning

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Sparse Representations for Object- and Ego-Motion Estimations in Dynamic Scenes

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IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS 2021年第6期32卷 2521-2534页

作者： Kashyap, Hirak J. Fowlkes, Charless C. Krichmar, Jeffrey L. Univ Calif Irvine Dept Comp Sci Irvine CA 92697 USA Univ Calif Irvine Dept Cognit Sci Irvine CA 92697 USA

Disentangling the sources of visual motion in a dynamic scene during self-movement or ego motion is important for autonomous navigation and tracking. In the dynamic image segments of a video frame containing independently moving objects, optic flow relative to the next frame is the sum of the motion fields generated due to camera and object motion. The traditional ego-motion estimation methods assume the scene to be static, and the recent deep learning-based methods do not separate pixel velocities into object- and ego-motion components. We propose a learning-based approach to predict both ego-motion parameters and object-motion field (OMF) from image sequences using a convolutional autoencoder while being robust to variations due to the unconstrained scene depth. This is achieved by: 1) training with continuous ego-motion constraints that allow solving for ego-motion parameters independently of depth and 2) learning a sparsely activated overcomplete ego-motion field (EMF) basis set, which eliminates the irrelevant components in both static and dynamic segments for the task of ego-motion estimation. In order to learn the EMF basis set, we propose a new differentiable sparsity penalty function that approximates the number of nonzero activations in the bottleneck layer of the autoencoder and enforces sparsity more effectively than L1- and L2-norm-based penalties. Unlike the existing direct ego-motion estimation methods, the predicted global EMF can be used to extract OMF directly by comparing it against the optic flow. Compared with the state-of-the-art baselines, the proposed model performs favorably on pixelwise object- and ego-motion estimation tasks when evaluated on real and synthetic data sets of dynamic scenes.

关键词： Estimation Dynamics Optical imaging Cameras Training Adaptive optics Motion segmentation convolutional autoencoder ego motion object motion overcomplete basis sparse representation

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Efficient structurally-strengthened generative adversarial network for MRI reconstruction

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NEUROCOMPUTING 2021年 422卷 51-61页

作者： Zhou, Wenzhong Du, Huiqian Mei, Wenbo Fang, Liping Beijing Inst Technol Sch Informat & Elect Beijing 100081 Peoples R China Beijing Inst Technol Sch Math & Stat Beijing 100081 Peoples R China

Compressed sensing based magnetic resonance imaging (CS-MRI) methods greatly shorten the scanning time while ensuring the quality of image reconstruction in an efficient way. Recently deep learning has been introduced into MRI reconstruction to further improve the image quality and shorten reconstruction time. In this paper, we propose an efficient structurally-strengthened Generative Adversarial Network, termed as ESSGAN, for reconstructing MR images from highly under-sampled k-space data. ESSGAN con-sists of a structurally-strengthened generator (termed as SG) and a discriminator. In SG, we introduce strengthened connections to enhance feature propagation and reuse between the concatenated strengthened convolutional autoencoders (termed as SCAEs), where each SCAE is a variant of a typical convolutional autoencoder. In addition, we creatively introduce the residual in residual blocks (termed as RIRBs) to SG. The RIRBs can effectively enhance the expression ability of the proposed SG. To further preserve more image details, we introduce an enhanced structural loss to SG. The experimental results demonstrate that ESSGAN can provide higher image quality with fewer model parameters than the state-of-the-art deep learning-based methods at different undersampling rates under different types of undersampling patterns. (c) 2020 Elsevier B.V. All rights reserved.

关键词： Magnetic resonance imaging convolutional autoencoder Deep learning Residual connection GANs

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Input-Aware Sparse Tensor Storage Format Selection for Optimizing MTTKRP

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IEEE TRANSACTIONS ON COMPUTERS 2021年第8期71卷 1968-1981页

作者： Sun, Qingxiao Liu, Yi Yang, Hailong Dun, Ming Luan, Zhongzhi Gan, Lin Yang, Guangwen Qian, Depei Beihang Univ Sch Comp Sci & Engn Beijing 100191 Peoples R China Beihang Univ Sch Cyber Sci & Technol Beijing 100191 Peoples R China Tsinghua Univ Dept Comp Sci & Technol Beijing 100084 Peoples R China

Canonical polyadic decomposition (CPD) is one of the most common tensor computations adopted in many scientific applications. The major bottleneck of CPD is matricized tensor times Khatri-Rao product (MTTKRP). To optimize the performance of MTTKRP, various sparse tensor formats have been proposed such as CSF and HiCOO. However, due to the spatial complexity of the tensors, no single format fits all tensors. To address this problem, we propose SpTFS, a framework that automatically predicts the optimal storage format for an input sparse tensor. Specifically, SpTFS leverages a set of sampling methods to lower the sparse tensor to fixed-sized matrices and sparsity features. In addition, SpTFS adopts both supervised learning based and unsupervised learning based methods to predict the optimal sparse tensor storage formats. For supervised learning, we propose TnsNet that combines convolution neural network (CNN) and the feature layer, which effectively captures the sparsity patterns of the input tensors. Once trained, the TnsNet can be used with either density or histogram representation of the input tensor for optimal format prediction. Whereas for unsupervised learning, we propose TnsClustering that consists of a feature encoder using convolutional layers and fully connected layers, and a K-means++ model to cluster sparse tensors for optimal tensor format prediction, without massively profiling on the hardware platform. SpTFS can use the above two models to predict the optimal tensor storage format for accelerating MTTKRP accurately. The experimental results show that both TnsNet and TnsClustering can achieve higher prediction accuracy and performance speedup compared to the state-of-the-art works.

关键词： Tensors Sparse matrices Hardware convolutional neural networks Matrix decomposition Unsupervised learning Task analysis Tensor computation MTTKRP sparse tensor storage format convolutional neural network convolutional autoencoder

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Likelihood-based feature representation learning combined with neighborhood information for predicting circRNA-miRNA associations

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BRIEFINGS IN BIOINFORMATICS 2024年第2期25卷 bbae020-bbae020页

作者： Guo, Lu-Xiang Wang, Lei You, Zhu-Hong Yu, Chang-Qing Hu, Meng-Lei Zhao, Bo-Wei Li, Yang Northwestern Polytech Univ Xian Peoples R China China Univ Min & Technol Xuzhou Peoples R China Xijing Univ Xian Peoples R China Peking Univ Beijing Peoples R China Univ Chinese Acad Sci Beijing Peoples R China Hefei Univ Technol Hefei Peoples R China

Connections between circular RNAs (circRNAs) and microRNAs (miRNAs) assume a pivotal position in the onset, evolution, diagnosis and treatment of diseases and tumors. Selecting the most potential circRNA-related miRNAs and taking advantage of them as the biological markers or drug targets could be conducive to dealing with complex human diseases through preventive strategies, diagnostic procedures and therapeutic approaches. Compared to traditional biological experiments, leveraging computational models to integrate diverse biological data in order to infer potential associations proves to be a more efficient and cost-effective approach. This paper developed a model of convolutional autoencoder for CircRNA-MiRNA Associations (CA-CMA) prediction. Initially, this model merged the natural language characteristics of the circRNA and miRNA sequence with the features of circRNA-miRNA interactions. Subsequently, it utilized all circRNA-miRNA pairs to construct a molecular association network, which was then fine-tuned by labeled samples to optimize the network parameters. Finally, the prediction outcome is obtained by utilizing the deep neural networks classifier. This model innovatively combines the likelihood objective that preserves the neighborhood through optimization, to learn the continuous feature representation of words and preserve the spatial information of two-dimensional signals. During the process of 5-fold cross-validation, CA-CMA exhibited exceptional performance compared to numerous prior computational approaches, as evidenced by its mean area under the receiver operating characteristic curve of 0.9138 and a minimal SD of 0.0024. Furthermore, recent literature has confirmed the accuracy of 25 out of the top 30 circRNA-miRNA pairs identified with the highest CA-CMA scores during case studies. The results of these experiments highlight the robustness and versatility of our model.

关键词： circRNA miRNA circRNA-miRNA association convolutional autoencoder natural language processing deep neural networks

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HYPERSPECTRAL CLUSTERING USING ATROUS SPATIAL-SPECTRAL convolutional NETWORK

HYPERSPECTRAL CLUSTERING USING ATROUS SPATIAL-SPECTRAL CONVO...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Shahi, Kasra Rafiezadeh Ghamisi, Pedram Rasti, Behnood Scheunders, Paul Gloaguen, Richard Univ Antwerp Imec Visionlab Dept Phys B-2000 Antwerp Belgium Helmholtz Inst Freiberg Resource Technol Helmholtz Zentrum Dresden Rossendorf D-09599 Freiberg Germany

ISBN: (数字)9781665427920

ISBN: (纸本)9781665427920

Hyperspectral imaging is an important technology in the field of geosciences and remote sensing. However, the high-dimensional nature of hyperspectral images (HSIs) together with the limited availability of training/labeled samples challenge an efficient processing of HSIs. To alleviate these challenges, we propose a deep multi-resolution clustering network (DMC-Net) to analyze HSIs. DMC-Net, without requiring training/labeled samples for the training process, captures the non-linear intrinsic relation within data points in an HSI and analyzes the image at various resolutions by applying atrous convolutions. Furthermore, DMC-Net preserves the spectral information by directly incorporating extracted features from the original HSI into the reconstruction phase. In terms of clustering accuracy, experimental results on two real HSIs demonstrate the superior performance of DMC-Net compared to the state-of-the-art deep learning-based clustering approaches.

关键词： Hyperspectral Imagery Multi-resolution Analysis Deep Clustering convolutional autoencoder Atrous Convolutions

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Unsupervised Abnormal Sensor Signal Detection With Channelwise Reconstruction Errors

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IEEE ACCESS 2021年 9卷 39995-40007页

作者： Kwak, Mingu Kim, Seoung Bum Korea Univ Sch Ind Management Engn Seoul 02841 South Korea

Detecting an anomaly in multichannel signal data is a challenging task in various domains. It should take into account the cross-channel relationship and temporal relationship within each channel. Moreover, the signal data is high-dimensional and making it difficult to gather sufficient abnormal labels. Consequently, unsupervised reconstruction-based anomaly detection methods have been applied successfully in many studies. However, they lose valuable channel information inherent in the reconstruction errors by merely averaging the errors for both the channel and time, then consider the average value as an anomaly score. In this study, we propose a method to explicitly employ channelwise reconstruction errors as a feature to detect abnormal signals. After a convolutional autoencoder produces the channelwise reconstruction errors, a machine learning anomaly detection model aggregates the errors as an anomaly score. To demonstrate the effectiveness and applicability of the proposed model, we conduct experiments using simulated data and real-world automobile data. The results show that the proposed method remarkably enhances the detectability compared to the simple average of the reconstruction errors. The reconstruction errors of abnormal and normal channels are shown to be different;therefore, it can be considered as an appropriate feature for anomaly detection. The best performance is obtained by using local outlier factors in the following anomaly detection model.

关键词： Anomaly detection Image reconstruction Feature extraction Data models Time series analysis Principal component analysis Convolution Anomaly detection convolutional autoencoder deep learning multichannel sensor signal data unsupervised learning

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