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Deep learning-based multiomics integration model for predicting axillary lymph node metastasis in breast cancer

FUTURE ONCOLOGY 2023年第20期19卷 1429-1438页

作者： Li, Xue Yang, Lifeng Jiao, Xiong Taiyuan Univ Technol Coll Biomed Engn Jinzhong 030600 Shanxi Peoples R China Taiyuan Univ Technol Coll Comp Sci & Technol Jinzhong 030600 Shanxi Peoples R China

Aim: To develop a deep learning-based multiomics integration model. Materials & methods: Five types of omics data (mRNA, DNA methylation, miRNA, copy number variation and protein expression) were used to build a deep learning-based multiomics integration model via a deep neural network, incorporating an attention mechanism that adaptively considers the weights of multiomics features. Results: Compared with other methods, the deep learning-based multiomics integration model achieved remarkable results, with an area under the curve of 0.89 (95% CI: 0.863-0.910). Conclusion: The deep learning-based multiomics integration model achieved promising results and is an effective method for predicting axillary lymph node metastasis in breast cancer.

关键词： axillary lymph node metastasis breast cancer deep learning multiomics variational autoencoder

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ECG-grained Cardiac Monitoring Using RFID 33

ECG-grained Cardiac Monitoring Using RFID

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33rd IEEE International Conference on Computer Communications and Networks (ICCCN)

作者： Wang, Ningning Zhao, Tianya Mao, Shiwen Bai, Harrison X. Jiao, Zhicheng Wang, Xuyu Florida Int Univ Knight Fdn Sch Comp & Informat Sci Miami FL 33199 USA Auburn Univ Dept Elect & Comp Engn Auburn AL 36849 USA Johns Hopkins Univ Dept Radiol & Radiol Sci Sch Med Baltimore MD 21287 USA Rhode Isl Hosp Dept Diagnost Imaging Providence RI 02903 USA

ISBN: (纸本)9798350348439;9798350384611

Heartbeat signals are useful to disease prediction, sub-health diagnosis, fatigue warning, and even emotion estimation. There is a compelling need for contactless, easy-to-deploy, and long-term heartbeat monitoring. This paper presents a contactless Radio Frequency Identification (RFID) based system for heartbeat monitoring that leverages the insight that RFID signal fluctuations induced by chest motion are synchronous with both respiration and heartbeat. The proposed system collects the temporal phase information from the tag pair on the body to extract heartbeat signals using a sequence of signal processing techniques. We propose a signal separation method based on empirical mode decomposition (EMD) to obtain heart rate after preprocessing. Furthermore, the estimated signal is input to an enhanced variational autoencoder (VAE) model to recover the heartbeat waveform. Implemented with commercial off-the-shelf (COTS) RFID devices, the system achieves accurate heart rate monitoring with less than 3% relative errors. The detected waveform exhibits a median cosine similarity of 0.83 as compared with the ground truth, which validate the system's wide applicability and high reliability for fine-grained, contactless heartbeat monitoring.

关键词： Radio Frequency Identification (RFID) vital sign monitoring contact-free sensing variational autoencoder

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LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any Singing Voice Conversion with Singer Guidance 25

LDM-SVC: Latent Diffusion Model Based Zero-Shot Any-to-Any S...

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25th Interspeech Conference

作者： Chen, Shihao Gu, Yu Zhang, Jie Li, Na Chen, Rilin Chen, Liping Dai, Lirong Univ Sci & Technol China USTC NERC SLIP Hefei Peoples R China Tencent AI Lab Shenzhen Peoples R China

Any-to-any singing voice conversion (SVC) is an interesting audio editing technique, aiming to convert the singing voice of one singer into that of another, given only a few seconds of singing data. However, during the conversion process, the issue of timbre leakage is inevitable: the converted singing voice still sounds like the original singer's voice. To tackle this, we propose a latent diffusion model for SVC (LDM-SVC) in this work, which attempts to perform SVC in the latent space using an LDM. We pretrain a variational autoencoder structure using the noted open-source So-VITS-SVC project based on the VITS framework, which is then used for the LDM training. Besides, we propose a singer guidance training method based on classifier-free guidance to further suppress the timbre of the original singer. Experimental results show the superiority of the proposed method over previous works in both subjective and objective evaluations of timbre similarity.

关键词： Singing voice conversion latent diffusion model variational autoencoder classifier-free guidance

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Detailed and Controllable Old Photo Restoration with Diffusion Priors 19

Detailed and Controllable Old Photo Restoration with Diffusi...

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19th IEEE International Symposium on Broadband Multimedia Systems and Broadcasting (BMSB)

作者： Liu, Xibei Wang, Han Wang, Yiwen Zhang, Yuhong Song, Jiayi Xie, Rong Song, Li Shanghai Jiao Tong Univ Inst Image Commun & Network Engn Shanghai Peoples R China Amer Straight A Acad Dallas TX USA Shanghai Jiao Tong Univ Cooperat Medianet Innovat Ctr CMIC Shanghai Peoples R China

ISBN: (纸本)9798350364279;9798350364262

Restoring old photos that contain numerous unknown and complex defects is a challenging and ill-posed problem. Traditional methods often struggle to address both structured and unstructured defects in real old photos, frequently leading to over-smoothed and uncompleted results. In this paper, we exploit powerful diffusion priors to construct a novel solution for the restoration of old photos. Our framework begins with a coarse stage model aimed at eliminating unstructured defects, followed by a conditional diffusion model that further refines the content and enriches details. Specifically, we introduce an edge control module, designed to encode the restored edge map into the denoising network. This integration effectively guides the restoration process, allowing the edge conditional diffusion to achieve more precise and controllable results. Additionally, we incorporate a feature fusion VAE to ensure the fidelity of the final outputs. Through extensive qualitative, quantitative, and ablative experiments, we demonstrate the innovation and effectiveness of the proposed method, which offers a complete, detailed, and personalized restoration of old photos.

关键词： Old Photo Restoration Edge Condition variational autoencoder Diffusion Model

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Enhancing Bandwidth Efficiency for Video Motion Transfer Applications Using Deep Learning Based Keypoint Prediction 25th

Enhancing Bandwidth Efficiency for Video Motion Transfer App...

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25th International Conference on Engineering Applications of Neural Networks (EANN)

作者： Bai, Xue Haque, Tasmiah Mohan, Sumit Cai, Yuliang Jeong, Byungheon Halasz, Adam Das, Srinjoy West Virginia Univ Morgantown WV 26506 USA Intel Corp Santa Clara CA USA Univ Southern Calif Los Angeles CA 90089 USA Coupa Software San Mateo CA USA

ISBN: (纸本)9783031624940;9783031624957

We propose a deep learning based novel prediction framework for enhanced bandwidth reduction in motion transfer enabled video applications such as video conferencing, virtual reality gaming and privacy preservation for patient health monitoring. To model complex motion, we use the First Order Motion Model (FOMM) that represents dynamic objects using learned keypoints along with their local affine transformations. Keypoints are extracted by a self-supervised keypoint detector and organized in a time series corresponding to the video frames. Prediction of keypoints, to enable transmission using lower frames per second on the source device, is performed using a variational Recurrent Neural Network (VRNN). The predicted keypoints are then synthesized to video frames using an optical flow estimator and a generator network. This efficacy of leveraging keypoint based representations in conjunction with VRNN based prediction for both video animation and reconstruction is demonstrated on three diverse datasets. For real-time applications, our results show the effectiveness of our proposed architecture by enabling up to 2x additional bandwidth reduction over existing keypoint based video motion transfer frameworks without significantly compromising video quality.

关键词： Motion transfer Bandwidth reduction Recurrent Neural Network variational Recurrent Neural Network variational autoencoder Keypoints

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3D Artistic Images Generation from 2D Images Using Improved Wasserstein Generative Adversarial Networks 8th

3D Artistic Images Generation from 2D Images Using Improved ...

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2024 World Congress in Computer Science, Computer Engineering and Applied Computing-CSCE

作者： Esan, Dorcas Oladayo Owolawi, Pius Adewale Tu, Chunling Tshwane Univ Technol Dept Comp Syst Engn Pretoria South Africa

ISBN: (纸本)9783031856273;9783031856280

Excellent artistic work frequently showcases the individual inventiveness of the creator, and many artists place a high value on producing images of superior quality. A remarkable success has been achieved by GANs in producing photorealistic 2-D images. Because traditional GANs generate 3D images directly from 2D, their images frequently lack the perfect quality and consistency of multiple views from a single image. With varied degrees of success, numerous attempts have been made to address these issues. With the use of a 3D variational autoencoder (VAE) that integrated Improved Wasserstein Generative Adversarial Networks (3D VAE-IWGAN), this research aims to produce high-quality and consistent multi-view three-dimensional (3D) artistic images. The experiments were conducted using the COCO Africa Mask dataset, and the suggested method produced high-quality images that were more closely aligned with the aesthetic qualities of authentic works of art than other complex methods. In comparison to other methods, the proposed method has a high Inception Score(IS) score of 26.37, a low frechet inception distance (FID) score of 12.09, a peak signal-to-noise ratio (PSNR) of 31.35, a mean squared error (MSE) of 65.32, a chamfer distance (CD) of 0.243, Learned Perceptual Image Patch Similarity (LPIPS) score of 0.134 and a structural similarity index measure (SSIM) of 0.772. These results are better than those obtained with other methods on the COCO Africa Mask dataset. The suggested approach can help artists showcase, develop, and improve the originality of their work, which will increase their financial gains.

关键词： Generative Adversarial Networks 3-D Image variational autoencoder artistic Image Improved Wasserstein GAN

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Health Indicator for Low-Speed Axial Bearings Using variational autoencoders

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IEEE ACCESS 2020年 8卷 35842-35852页

作者： Hemmer, Martin Klausen, Andreas Huynh Van Khang Robbersmyr, Kjell G. Waag, Tor, I Univ Agder Dept Engn Sci N-4877 Grimstad Norway MHWirth AS N-4639 Kristiansand Norway NORCE Norwegian Res Ctr AS N-4877 Grimstad Norway

This paper proposes a method for calculating a health indicator (HI) for low-speed axial rolling element bearing (REB) health assessment by utilizing the latent representation obtained by variational inference using variational autoencoders (VAEs), trained on each speed reference in the dataset. Further, versatility is added by conditioning on the speed, extending the VAE to a conditional VAE (CVAE), thereby incorporating all speeds in a single model. Within the framework, the coefficients of autoregressive (AR) models are used as features. The dimensionality reduction inherent in the proposed method lowers the need of expert knowledge to design good condition indicators. Moreover, the suggested methodology allows for setting the probability of false alarms when encoding new data points to the latent variable space using the trained model. The effectiveness of the proposed method is validated based on two different datasets: from a workshop test of an offshore drilling machine and from an in-house test rig for axial bearings. In both datasets, the HI is exceeding the warning and alarm levels with a probability of false alarm (PFA) of 10(-6), and the method is most effective at lower shaft speeds.

关键词： Training Decoding Dimensionality reduction Drilling machines Fault detection Industries Rolling bearings Bearing fault detection condition monitoring unsupervised learning variational autoencoder conditional variational autoencoder generative models

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Discovering web services in social web service repositories using deep variational autoencoders

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INFORMATION PROCESSING & MANAGEMENT 2020年第4期57卷 102231-102231页

作者： Lizarralde, Ignacio Mateos, Cristian Zunino, Alejandro Majchrzak, Tim A. Gronli, Tor-Morten UNICEN CONICET Tandil ISISTAN Buenos Aires DF Argentina Univ Agder Kristiansand Norway Kristiania Univ Coll Oslo Norway

Web Service registries have progressively evolved to social networks-like software repositories. Users cooperate to produce an ever-growing, rich source of Web APIs upon which new value-added Web applications can be built. Such users often interact in order to follow, comment on, consume and compose services published by other users. In this context, Web Service discovery is a core functionality of modern registries as needed Web Services must be discovered before being consumed or composed. Many efforts to provide effective keyword-based service discovery mechanisms are based on Information Retrieval techniques as services are described using structured or unstructured textdocuments that specify the provided functionality. However, traditional techniques suffer from term-mismatch, which means that only the terms that are contained in both user queries and descriptions are exploited to perform service retrieval. Early feature learning techniques such as LSA or LDA tried to solve this problem by finding hidden or latent features in text documents. Recently, alternative feature learning based techniques such as Word Embeddings achieved state of the art results for Web Service discovery. In this paper, we propose to learn features from service descriptions by using variational autoencoders, a special kind of autoencoder which restricts the encoded representation to model latent variables. autoencoders in turn are deep neural networks used for unsupervised learning of efficient codings. We train our autoencoder using a real 17 113-service dataset extracted from the *** API social repository. We measure discovery efficacy by using both Recall and Precision metrics, achieving significant gains compared to both Word Embeddings and classic latent features modelling techniques. Also, performance-oriented experiments show that the proposed approach can be readily exploited in practice.

关键词： Service-oriented computing Web Services Service discovery Deep neural network variational autoencoder

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High-dimensional numerical optimization of fiber reinforced polymers with variational autoencoders and Bayesian optimization

High-dimensional numerical optimization of fiber reinforced ...

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作者： Storm, Joep Delft University of Technology

学位级别：硕士

Designing engineering structures relies on accurate numerical simulations to predict the behaviour of a structure before its realization. In the design process, many variables influence the final structure. There is an incentive for optimizing the design based on the desire for cheaper structures and less material usage. Although a wide variety of optimization techniques exist, in practice many structures are not fully optimized and could fulfill their purpose with less material usage. This partly results from optimization techniques generally requiring many simulations for problems with a high number of variables. In creating increasingly complex models, simulating its performance can take up to days or weeks to compute, incentivizing the development of optimization methods that use as little simulations as possible. The goal of this thesis is to provide a method of optimization which significantly reduces the required number of simulations to be performed, aiming to overcome the issue of required computational effort. The optimization problem used in this thesis is the geometric optimization of a fiber reinforced polymer (FRP) microstructure. Each evaluation requires a Finite Element analysis, and many parameters are required to describe the geometry. The goal is to find the maximum stress at perfect plasticity during uniaxial tension, interpreted as a measure of strength. While generally only fibers with a circular cross-section are used, here a morphing parameter is introduced that changes this shape between being circular and square. Furthermore, fibers are allowed to overlap, effectively creating a single fiber with a complex shape. This geometry optimization provides a challenging problem for which different optimization techniques can be compared. The approach taken in attempting to minimize the number of function evaluations is a combination of several machine learning methods. Based on a limited number of initial samples, Bayesian optimization (BO) is appl

关键词： Bayesian Optimization variational autoencoder machine learning Regression high-dimensional Fiber Reinforced Polymers - FRP Artificial intelligence optimization Genetic Algorithm - GA Bayesian neural network Neural Network

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Non-episodic variational Discriminative Few-Shot Classifier 1

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9th International Conference on Pattern Recognition and Machine Intelligence (PReMI)

作者： Padma, V. Bhagvati, Chakravarthy Univ Hyderabad Sch Comp & Informat Sci Hyderabad India

ISBN: (数字)9783031127007

ISBN: (纸本)9783031126994;9783031127007

Few-shot learning techniques enable deep learning models to identify novel classes from one or only a few examples of it. This is achieved by using the subconcepts learnt from pre-training on base classes that share some of the characteristics of the novel classes. Most of the existing approaches use episodic training or episodic fine tuning (N-way K-shot) to learn these subconcepts. However, most studies in literature require different training models that are tied to the different N-way K-shot test settings;N represents the number of classes and K represents the number of samples in each class. Second, these models are trained for a large number of episodes and the memory requirements are also high for increasing values of N and K. In contrast, we propose a method that combines a beta-variational autoencoder with cosine similarity classifier trained only once in an end-to-end (non-episodic) fashion on the complete training set. The end-to-end training enables the model to learn disentangled content and style representation. After training, the weights of novel classes are generated by normalizing the encoder's content representation. Now the model can perform any N-way K-shot classification. Unlike the majority of results reported in literature, our work includes the results for the case where the test set includes both base and novel class images. We tested our model on the Omniglot dataset. Our model performed on par with the state-of-the-art methods. In a few settings it also outperformed the existing methods.

关键词： Few shot learning variational autoencoder Cosine similarity classifer One shot learning

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