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IEEE International Ultrasonics Symposium (IEEE IUS)

作者： Fouad, Mariam Abd El Ghany, Mohamed A. Huebner, Michael Schmitz, Georg Ruhr Univ Bochum Bochum Germany Brandenburg Tech Univ Cottbus Senftenberg Germany German Univ Cairo Cairo Egypt Tech Univ Darmstadt Integrated Elect Syst Lab Darmstadt Germany

ISBN: (纸本)9780738112091

High resultant image contrast and quality have caused tissue harmonic imaging to become a valuable tool in ultrasound imaging. Amplitude Modulation (AM) is one of the most commonly used nonlinear pulsing schemes in tissue harmonic imaging. However, its need for at least two consecutive firings continues to be a hindering factor for a faster imaging process. In this work, deep learning concepts are exploited to introduce an alternative approach for ultrasound tissue harmonic imaging using a single firing. This is achieved by implementing an asymmetric convolutional autoencoder network to estimate the low-harmonic component content from a received echo signal. The network is trained on the full-amplitude harmonic content IQ echo comprising the network's input and its corresponding low-amplitude harmonic IQ echo representing the network's output. The proposed approach yielded high contrast harmonic images with comparable contrast to noise ratio and contrast ratio to the conventional checkerboard apertures amplitude modulation technique, yet at approximately three times the frame rate. Moreover, less clutter is observed in the proposed approach reconstructed images in contrast with the ground truth images. These results open the door for the implementation of harmonic imaging with comparable quality to the conventional AM techniques, yet with an increased framerate and reduced motion artifacts.

关键词： Deep learning convolutional autoencoders Harmonic Imaging

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Metal surface 3D imaging based on convolutional autoencoder and light plane calibration

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OPTIK 2022年 259卷

作者： Tan, Zhiying Zhao, Baolai Wang, Wen Xu, Xiaobin Luo, Minzhou Hohai Univ Coll Mech & Elect Engn Changzhou 213022 Jiangsu Peoples R China Hohai Univ Jiangsu Key Lab Special Robot Technol Changzhou 213022 Jiangsu Peoples R China Jiangsu JITRI Intelligent Mfg Technol Res Inst Co Changzhou 211800 Jiangsu Peoples R China

convolutional autoencoders play an important role in computer vision and image processing. In this paper, an optical stripe extraction method based on convolutional autoencoder is proposed to reconstruct reflective metal surfaces. A six-layer convolutional autoencoder network is constructed to extract the center line of the light stripe. In addition, combined with the center line extraction algorithm and line-structured light calibration algorithm, a good reconstruction effect under reflective conditions is obtained. Experimental results show that the relative error of the 3D imaging method based on the convolutional autoencoder is decreased from 6.233% to 2.183% compared with the traditional line-structured laser 3D imaging method.

关键词： convolutional autoencoders 3D imaging Line-structured light calibration Optical stripe extraction method

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A novel unsupervised approach based on the hidden features of Deep Denoising autoencoders for COVID-19 disease detection

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EXPERT SYSTEMS WITH APPLICATIONS 2022年 192卷 116366-116366页

作者： Scarpiniti, Michele Ahrabi, Sima Sarv Baccarelli, Enzo Piazzo, Lorenzo Momenzadeh, Alireza Sapienza Univ Rome Dept Informat Engn Elect & Telecommun DIET Via Eudossiana 18 I-00184 Rome Italy

Chest imaging can represent a powerful tool for detecting the Coronavirus disease 2019 (COVID-19). Among the available technologies, the chest Computed Tomography (CT) scan is an effective approach for reliable and early detection of the disease. However, it could be difficult to rapidly identify by human inspection anomalous area in CT images belonging to the COVID-19 disease. Hence, it becomes necessary the exploitation of suitable automatic algorithms able to quick and precisely identify the disease, possibly by using few labeled input data, because large amounts of CT scans are not usually available for the COVID-19 disease. The method proposed in this paper is based on the exploitation of the compact and meaningful hidden representation provided by a Deep Denoising convolutional Autoencoder (DDCAE). Specifically, the proposed DDCAE, trained on some target CT scans in an unsupervised way, is used to build up a robust statistical representation generating a target histogram. A suitable statistical distance measures how this target histogram is far from a companion histogram evaluated on an unknown test scan: if this distance is greater of a threshold, the test image is labeled as anomaly, i.e. the scan belongs to a patient affected by COVID-19 disease. Some experimental results and comparisons with other state-of-the-art methods show the effectiveness of the proposed approach reaching a top accuracy of 100% and similar high values for other metrics. In conclusion, by using a statistical representation of the hidden features provided by DDCAEs, the developed architecture is able to differentiate COVID-19 from normal and pneumonia scans with high reliability and at low computational cost.

关键词： Deep denoising autoencoder convolutional autoencoders Feature learning Computed Tomography (CT) Coronavirus disease 2019 COVID-19 Pneumonia Anomaly detection

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Machine learning accelerated transient analysis of stochastic nonlinear structures

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ENGINEERING STRUCTURES 2022年 257卷 114020-114020页

作者： Nikolopoulos, Stefanos Kalogeris, Ioannis Papadopoulos, Vissarion Natl Tech Univ Athens Inst Struct Anal & Antiseism Res Engn Simulat Lab MGrp Zografou Campus9 Iroon Polytech Str Zografos 15780 Greece

This paper presents a non-intrusive surrogate modeling scheme for transient response analysis of nonlinear structures involving random parameters. The proposed scheme utilizes a two-level neural network architecture as a surrogate model. Specifically, it combines feed-forward neural networks with convolutional autoencoders to deliver a highly accurate and inexpensive emulator of the structural system under investigation. The surrogate is built upon an initial set of full model evaluations, which are performed for a small, yet sufficient number of parameter values and serve as the training data set. For each type of degree of freedom in the structural problem, a convolutional autoencoder is trained over the corresponding solution matrices in order to obtain a low-dimensional vector representation through its encoder and a reconstruction map by the decoder. Subsequently, a feed forward neural network is efficiently trained to map points from the problem's parametric space to the latent space given by the encoder, which can be further mapped to the actual, high-dimensional, system responses by the decoder mapping. The proposed surrogate is capable of predicting the entire time history response almost instantaneously and with remarkable accuracy, despite the nonlinearities present in the system's response. The elaborated methodology is demonstrated on the stochastic nonlinear transient analysis of single and multiple degree of freedom structural systems.

关键词： Surrogate modeling Feed forward neural networks convolutional autoencoders Nonlinear transient analysis Stochastic analysis Monte Carlo simulation

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A 3D-CAE-CNN model for Deep Representation Learning of 3D images

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ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 2022年第0期113卷

作者： Pintelas, Emmanuel Pintelas, Panagiotis Univ Patras Dept Math GR-26500 Patras Greece

Deep Representation Learning technologies based on supervised convolutional Neural Networks (CNNs) have attained significant interest mainly due to their superior performance for learning abstract and robust features used in object detection and image classification tasks. However, to efficiently train such models requires a large number of labeled instances especially when these instances are high dimensional such as for 3-Dimensional (3D) Image inputs. Due to this extra dimension the dimensionality of such instances increases drastically. Therefore, the utilization of Unsupervised CNNs topologies such 3D convolutional autoencoders (3D-CAE) have also been proposed. CAEs can learn features (and later used for classification tasks using common machine learning classifiers), without relying on instance labels and thus they are not prone to label limitation. Nevertheless, it is not clear if the features that CAEs learn, are relevant regarding the classification or object detection task since these features are learned via no target output class. For these reasons, in this work we combine 3D-CAE and 3D-CNN to work synergistically together in order to build a hybrid deep representation learning framework model which exploits the advantages of both unsupervised and supervised representation/feature learning approaches, applied on 3D Image inputs. In order to evaluate our strategy, we performed extensive experimental simulations for the DeepFake and Pneumonia detection problems utilizing Video and 3D Scans datasets respectively. Our proposed framework outperformed all the other utilized frameworks, revealing the efficiency of our applied methodology.

关键词： Deep Representation Learning convolutional autoencoders Deep convolutional neural networks 3D image classification DeepFake video detection 3D Pneumonia detection

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INTERPOLATION OF IRREGULARLY SAMPLED FREQUENCY RESPONSE FUNCTIONS USING convolutional NEURAL NETWORKS

INTERPOLATION OF IRREGULARLY SAMPLED FREQUENCY RESPONSE FUNC...

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

作者： Acerbi, M. Malvermi, R. Pezzoli, M. Antonacci, F. Sarti, A. Corradi, R. Politecn Milan Dept Elect Informat & Bioengn Milan Italy Politecn Milan Dept Mech Engn Milan Italy

ISBN: (纸本)9781728176055

In the field of structural mechanics, classical methods for the vibrational characterization of objects exploit the inherent redundancy of a relevant amount of measurements acquired over regular sampling grids. However, there are cases in which parts of the objects under analysis are not accessible with sensors, leading to irregular sampling grids characterized by holes. Recent works have proved the benefits of adding prior knowledge in these scenarios, either through the definition of a suitable decomposition or using Finite Element modelling. In this paper we propose to use convolutional autoencoders (CA) for Frequency Response Function (FRF) interpolation from grids with different subsampling schemes. CA learn a compressed representation from a dataset of FRFs synthetized through Finite Element Analysis. Experiments with numerical and experimental data show the effectiveness of the model with a different amount of missing data and its ability to predict real FRFs characterized by different damping and sampling frequency.

关键词： Spatial Interpolation Frequency Response Function Irregular Sampling convolutional autoencoders

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autoencoders and their application in removing masks

Autoencoders and their application in removing masks

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作者： Zixiang Liu Maths department of Imperial College London

Images are frequently distorted by noises that have a negative impact on the quality of image *** this study,the author focuses on coping with a specific type of noise that has arisen regularly in recent years as a result of the pandemic:masks covering portions of the photographs of human *** paper employs the autoencoder model,which offers unsupervised *** compresses or encodes original data input into a smaller latent vector,then decodes it back to its original size,learning and extracting relevant features from the data in the *** a further phase,the author employs a combination of convolutional autoencoders and denoising autoencoders,treating masks as corruptions in order to get more accurate predictions regarding the image of a human face without any *** training on 2,500 image pairs with and without masks and validating on 200 such image pairs,the model presented in this research achieves an overall accuracy of 93%.The research demonstrates that the combination of convolutional and denoising autoencoders is an excellent method for removing masks from facial images,and the author believes it can also be used to effectively remove other types of ***,the study also reveals that the picture data generated in this manner are always inferior to the original,and that the autoencoder can only process data of the same or comparable type on which it has been *** the future,improved models will exist to address these shortcomings and be applied to more real-life situations.

关键词： convolutional autoencoders denoising autoencoders denoising image reconstruction mask-removing

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Parallel Representation Learning for the Classification of Pathological Speech: Studies on Parkinson's Disease and Cleft Lip and Palate

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SPEECH COMMUNICATION 2020年 122卷 56-67页

作者： Vasquez-Correa, J. C. Arias-Vergara, T. Schuster, M. Orozco-Arroyave, J. R. Noeth, E. Friedrich Alexander Univ Erlangen Nuremberg Pattern Recognit Lab Erlangen Germany Univ Antioquia UdeA Fac Engn Medellin Colombia Ludwig Maximilians Univ Munchen Dept Otorhinolaryngol Munich Germany

Speech signals may contain different paralinguistic aspects such as the presence of pathologies that affect the proper communication capabilities of a speaker. Those speech disorders have different origin depending on the type of the disease. For instance, diseases with morphological origin such as cleft lip and palate that causes hypernasality, or with neurodegenerative origin such as Parkinson's disease that generates hypokinetic dysarthria on the patients. Automatic assessment of pathological speech allows to support the diagnosis and/or the evaluation of the disease severity. Conventional methods are based on the manually applied assessment of single features such as jitter, shimmer, or formant frequencies that may not completely model all of the phenomena that appear due to the disease. This paper introduces a novel strategy based on unsupervised representation learning for automatic detection of pathological speech. The proposed approach is based on the use of recurrent and convolutional autoencoders trained to extract informative features to characterize the presence of pathologies in speech. A novel feature set based on the reconstruction error of the autoencoders is also proposed. The performance of the introduced models is evaluated classifying pathological speech signals recorded from people suffering from Parkinson's disease, and children with cleft lip and palate. All participants from this study were Spanish native speakers. The proposed models are accurate to classify the speech signals of both kinds of diseases, with an accuracy of up to 97% for cleft lip and palate, and up to 84% for the case of Parkinson's disease. We also show that the reconstruction error from the autoencoders in different frequency regions contain information related to specific speech symptoms of both diseases.

关键词： Pathological speech Unsupervised representation learning convolutional autoencoders Recurrent autoencoders Parkinson's disease Cleft lip and palate

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A convolutional Autoencoder Topology for Classification in High-Dimensional Noisy Image Datasets

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SENSORS 2021年第22期21卷 7731-7731页

作者： Pintelas, Emmanuel Livieris, Ioannis E. Pintelas, Panagiotis E. Univ Patras Dept Math Patras 26500 Greece Core Innovat & Technol OE Athens 11745 Greece

Deep convolutional neural networks have shown remarkable performance in the image classification domain. However, Deep Learning models are vulnerable to noise and redundant information encapsulated into the high-dimensional raw input images, leading to unstable and unreliable predictions. autoencoders constitute an unsupervised dimensionality reduction technique, proven to filter out noise and redundant information and create robust and stable feature representations. In this work, in order to resolve the problem of DL models' vulnerability, we propose a convolutional autoencoder topological model for compressing and filtering out noise and redundant information from initial high dimensionality input images and then feeding this compressed output into convolutional neural networks. Our results reveal the efficiency of the proposed approach, leading to a significant performance improvement compared to Deep Learning models trained with the initial raw images.

关键词： convolutional autoencoders dimensionality reduction deep learning convolutional neural networks computer vision image classification

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PRIVACY-PRESERVING IMAGE SHARING VIA SPARSIFYING LAYERS ON convolutional GROUPS

PRIVACY-PRESERVING IMAGE SHARING VIA SPARSIFYING LAYERS ON C...

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

作者： Ferdowsi, Sohrab Razeghi, Behrooz Holotyak, Taras Calmon, Flavio P. Voloshynovskiy, Slava Univ Geneva Geneva Switzerland Harvard Univ Cambridge MA 02138 USA

ISBN: (纸本)9781509066315

We propose a practical framework to address the problem of privacy-aware image sharing in large-scale setups. We argue that, while compactness is always desired at scale, this need is more severe when trying to furthermore protect the privacy-sensitive content. We therefore encode images, such that, from one hand, representations are stored in the public domain without paying the huge cost of privacy protection, but ambiguated and hence leaking no discernible content from the images, unless a combinatorially-expensive guessing mechanism is available for the attacker. From the other hand, authorized users are provided with very compact keys that can easily be kept secure. This can be used to disambiguate and reconstruct faithfully the corresponding access-granted images. We achieve this with a convolutional autoencoder of our design, where feature maps are passed independently through sparsifying transformations, providing multiple compact codes, each responsible for reconstructing different attributes of the image. The framework is tested on a large-scale database of images with public implementation available.

关键词： privacy-preserving image sharing convolutional autoencoders sparse representation learned compression image obfuscation

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