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9th ACM International Conference on Bioinformatics, Computational Biology, and Health Informatics (ACM-BCB)

作者： Yang, Jingjing Cao, Renzhi Si, Dong Univ Washington Bothell WA 98011 USA Pacific Lutheran Univ Tacoma WA 98447 USA

ISBN: (纸本)9781450357944

Protein surface shape plays an essential role in various function of proteins. In order to efficiently investigate protein function and evolutionary history, we introduce a global protein surface shape representation called EMNets. EMNets provides an effective and accurate way of protein surface representation and similarity search, and thus contributes to biomedical research. The method uses a convolutional autoencoder (CAE) neural network to learn the geometric information of three-dimensional (3D) density maps in a data-driven manner. Our method effectively represents a 3D cryoelectron microscopy density map by using a descriptor consists of only 256 numeric variables which is called EMNets descriptor. Based on EMNets descriptor, we are able to retrieve similar protein surfaces using k-nearest-neighbor algorithm in real-time. The search results of protein surface represented with the EMNets descriptor has shown high agreement with the existing Combinatorial Extension (CE) algorithm of sequence and structure similarity search. Overall, EMNets is a powerful tool in comparing 3D protein structures obtained by cryo-electron microscopy.

关键词： protein surface similar retrieval convolutional autoencoder cryo-electron microscopy 3d object descriptor

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A convolutional autoencoder Approach for Feature Extraction in Virtual Metrology

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Procedia Manufacturing 2018年 17卷 126-133页

作者： Marco Maggipinto Chiara Masiero Alessandro Beghi Gian Antonio Susto Department of Information Engineering University of Padova Italy Human Inspired Technology Research Centre University of Padova Italy

Exploiting the huge amount of data collected by industries is definitely one of the main challenges of the so-called Big Data era. In this sense, Machine Learning has gained growing attention in the scientific community, as it allows to extract valuable information by means of statistical predictive models trained on historical process data. In Semiconductor Manufacturing, one of the most extensively employed data-driven applications is Virtual Metrology, where a costly or unmeasurable variable is estimated by means of cheap and easy to obtain measures that are already available in the system. Often, these measures are multi-dimensional, so traditional Machine Learning algorithms cannot handle them directly. Instead, they require feature extraction, that is a preliminary step where relevant information is extracted from raw data and converted into a design matrix. Features are often hand-engineered and based on specific domain knowledge. Moreover, they may be difficult to scale and prone to information loss, affecting the effectiveness and maintainability of machine learning procedures. In this paper, we present a Deep Learning method for semi-supervised feature extraction based on convolutional autoencoders that is able to overcome the aforementioned problems. The proposed method is tested on a real dataset for Etch rate estimation. Optical Emission Spectrometry data, that exhibit a complex bi-dimensional time and wavelength evolution, are used as input.

关键词： convolutional autoencoder Deep Learning Etching Feature Extraction Industry 4.0 Neural Network Optical Emission Spectroscopy Semiconductor Manufacturing

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DISEASE GRADING OF HETEROGENEOUS TISSUE USING convolutional autoencoder 14

DISEASE GRADING OF HETEROGENEOUS TISSUE USING CONVOLUTIONAL ...

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IEEE 14th International Symposium on Biomedical Imaging (ISBI) - From Nano to Macro

作者： Zerhouni, Erwan Prisacari, Bogdan Zhong, Qing Wild, Peter Gabrani, Maria IBM Res Zurich Saeumerstr 4 CH-8803 Ruschlikon Switzerland Univ Hosp Zurich Inst Surg Pathol Schmelzbergstr 12 CH-8091 Zurich Switzerland

ISBN: (纸本)9781509011728

One of the main challenges of histological image analysis is the high dimensionality of the images. This can be addressed via summarizing techniques or feature engineering. However, such approaches can limit the performance of subsequent machine learning models, particularly when dealing with highly heterogeneous tissue samples. One possible alternative is to employ unsupervised learning to determine the most relevant features automatically. In this paper, we propose a method of generating representative image signatures that are robust to tissue heterogeneity. At the core of our approach lies a novel deep-learning based mechanism to simultaneously produce representative image features as well as perform dictionary learning to further reduce dimensionality. By integrating this mechanism in a broader framework for disease grading, we show significant improvement in terms of grading accuracy compared to alternative local feature extraction methods.

关键词： Image Description convolutional autoencoder Tissue Heterogeneity Dimensionality Reduction

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3D intracranial artery segmentation using a convolutional autoencoder

3D intracranial artery segmentation using a convolutional au...

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Biological Ontologies and Knowledge Bases Workshop at IEEE International Conference on Bioinformatics and Biomedicine (IEEE BIBM)

作者： Chen, Li Xie, Yanjun Sun, Jie Balu, Niranjan Mossa-Basha, Mahmud Pimentel, Kristi Hatsukami, Thomas S. Hwang, Jenq-Neng Yuan, Chun Univ Washington Dept Elect Engn Seattle WA 98195 USA Univ Washington Dept Mech Engn Seattle WA 98195 USA Univ Washington Dept Radiol Seattle WA 98195 USA Univ Washington Dept Surg Seattle WA 98195 USA

ISBN: (纸本)9781509030507

Automated segmentation of intracranial arteries on magnetic resonance angiography (MRA) allows for quantification of cerebrovascular features, which provides tools for understanding aging and pathophysiological adaptations of the cerebrovascular system. Using a convolutional autoencoder (CAE) for segmentation is promising as it takes advantage of the autoencoder structure in effective noise reduction and feature extraction by representing high dimensional information with low dimensional latent variables. In this paper, we trained an 8-layer CAE to learn a 3D segmentation model of intracranial arteries from 49 cases of MRA data. After parameter optimization and prediction refinement, our trained model was shown to perform better than the three traditional segmentation methods in both binary classification and visual evaluation.

关键词： convolutional autoencoder deep neural network machine learning artery segmentation Magnetic Resonance Angiography

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Anomaly Detection of Logo Images in the Mobile Phone Using convolutional autoencoder 4

Anomaly Detection of Logo Images in the Mobile Phone Using C...

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4th International Conference on Systems and Informatics (ICSAI)

作者： Ke, Muyuan Lin, Chunyi Huang, Qinghua South China Univ Technol Dept Elect & Informat Engn Guangzhou Guangdong Peoples R China

ISBN: (纸本)9781538611074

There are Logo images printed in the mobile phone. Anomaly detection of Logo image is an important quality control task in the intelligent manufacture. In real applications, there are not enough negative samples in the production line for us to study their difference from normal samples. In this paper, we propose an unsupervised learning method based on convolutional autoencoder (CAE) to generate the template of sample and detect the abnormal information through comparing test images with the adaptive template. Firstly, several methods of data augmentation are introduced to expand the scale of positive samples, aiming to improve the performance of CAE. Secondly, the topology of proposed CAE model is introduced. Thirdly, we introduce the image processing methods to detect and locate the abnormal information in the Logo image. A series of experiments on three group of different Logo image have shown that the method we proposed can effectively detect most of the anomalies in the image and achieve the average accuracy of 98.9%.

关键词： Data Augmentation convolutional autoencoder Anomaly Detection Image Processing

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UNSUPERVISED IMAGE SEGMENTATION USING convolutional autoencoder WITH TOTAL VARIATION REGULARIZATION AS PREPROCESSING

UNSUPERVISED IMAGE SEGMENTATION USING CONVOLUTIONAL AUTOENCO...

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

作者： Wang, Chunlai Yang, Bin Liao, Yiwen Univ Stuttgart Inst Signal Proc & Syst Theory Stuttgart Germany

ISBN: (纸本)9781509041176

Conventional unsupervised image segmentation methods use color and geometric information and apply clustering algorithms over pixels. They preserve object boundaries well but often suffer from over-segmentation due to noise and artifacts in the images. In this paper, we contribute on a preprocessing step for image smoothing, which alleviates the burden of conventional unsupervised image segmentation and enhance their performance. Our approach relies on a convolutional autoencoder (CAE) with the total variation loss (TVL) for unsupervised learning. We show that, after our CAE-TVL preprocessing step, the over-segmentation effect is significantly reduced using the same unsupervised image segmentation methods. We evaluate our approach using the BSDS500 image segmentation benchmark dataset and show the performance enhancement introduced by our approach in terms of both increased segmentation accuracy and reduced computation time. We examine the robustness of the trained CAE and show that it is directly applicable to other natural scene images.

关键词： Image segmentation unsupervised convolutional autoencoder total variation loss

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LEARNING SENSOR-SPECIFIC FEATURES FOR HYPERSPECTRAL IMAGES VIA 3-DIMENSIONAL convolutional autoencoder 37

LEARNING SENSOR-SPECIFIC FEATURES FOR HYPERSPECTRAL IMAGES V...

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IEEE International Geoscience and Remote Sensing Symposium (IGARSS)

作者： Ji, Jingyu Mei, Shaohui Hou, Junhui Li, Xu Du, Qian Northwestern Polytech Univ Sch Elect & Informat Xian 710129 Shaanxi Peoples R China City Univ Hong Kong Dept Comp Sci Kowloon Hong Kong Peoples R China Mississippi State Univ Dept Elect & Comp Engn Mississippi State MS 39762 USA

ISBN: (纸本)9781509049516

Deep learning techniques have brought in revolutionary achievements for feature learning of images. In this paper, a novel structure of 3-Dimensional convolutional autoencoder (3D-CAE) is proposed for hyperspectral spatial-spectral feature learning, in which the spatial context is considered by constructing a 3-Dimensional input using pixels in a spatial neighborhood. All the parameters involved in the 3D-CAE are trained without the need of labeled training samples such that feature learning is conducted in an unsupervised fashion. Such unsupervised spatial-spectral feature extraction is also extended to different images from the same sensor to learn sensor-specific features. As a result, spatial-spectral features of hyperspectral images are extracted for a specific sensor under an unsupervised manner. Experimental results on several benchmark hyperspectral datasets have demonstrated that our proposed 3D-CAE are very effective in extracting sensor-specific spatial-spectral features and outperform several state-of-the-art deep learning neural networks in classification application.

关键词： hyperspectral classification deep learning convolutional autoencoder

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Unsupervised image segmentation using convolutional autoencoder with total variation regularization as preprocessing

Unsupervised image segmentation using convolutional autoenco...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Chunlai Wang Bin Yang Yiwen Liao Institute of Signal Processing and System Theory University of Stuttgart Germany

ISBN: (纸本)9781509041183

关键词： Image segmentation unsupervised convolutional autoencoder total variation loss image segmentation enhancing performance computational time image smoothing Pretreatment Unsupervised learning College of advanced education cyclophosphamide/doxorubicin/etoposide computer aided engineering

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convolutional Cross-Modal autoencoder-Based Few-Shot Learning for Data Augmentation with Application to Alzheimer Dementia Diagnosis

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COGNITIVE COMPUTATION 2025年第1期17卷 1-14页

作者： Bazine, Othmane Rai, Omar Aiadi, Oussama Hedjam, Rachid Khaldi, Belal Zhong, Guoqiang Univ Kasdi Merbah Comp Sci Ouargla 30000 Algeria Artificial Intelligence & Informat Technol Lab LIN Ouargla 30000 Algeria Bishops Univ Comp Sci Dept Sherbrooke PQ Canada Ocean Univ China Comp Sci & Technol 238 Songling Rd Qingdao 266100 Peoples R China

This paper presents a novel deep few-shot learning method for magnetic resonance images-based Alzheimer's dementia (AD) diagnosis. The proposed method consists of two main phases namely data augmentation and data classification. With regard to data augmentation and, to deal with data scarcity issues, we designed a convolutional cross-modal autoencoder (CCMAE) model for data generation. This model, which consists of two encoders and one decoder, receives two image modalities namely longitudinal and cross-section MRI, and generates a new cross-section image. We opt for a convolutional version of the autoencoder to capture the spatial information more effectively and reduce the number of trainable parameters. Moreover, to make the model able to perform deep analysis of input image, we establish a skip connection strategy between the first encoder and the decoder similar to the UNet mechanism. With regard to classification, we design a convolutional neural network-based model in which both textual and visual features are fused to strengthen the network performance and produce more reliable decisions. A comprehensive experiment on a publicly available dataset has been conducted to demonstrate the effectiveness of the proposed method compared to some related works. The code is publicly available at: https://***/Bazine-Othmane/scientific-paper-code.

关键词： Few-shot learning convolutional autoencoder convolutional neural networks Dementia Alzheimer Magnetic imaging resonance

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convolutional autoencoders for Anomaly Detection in Semiconductor Manufacturing 31

Convolutional AutoEncoders for Anomaly Detection in Semicond...

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31st Irish Conference on Artificial Intelligence and Cognitive Science (AICS)

作者： Gorman, Mark Ding, Xuemei Maguire, Liam Coyle, Damien Ulster Univ Global Factory IT Seagate Technol Intelligent Syst Res Ctr Derry North Ireland Ulster Univ Intelligent Syst Res Ctr Derry North Ireland Ulster Univ Intelligent Syst Res Ctr PVC Res Derry North Ireland Univ Bath Bath Inst Augmented Human Bath England

ISBN: (纸本)9798350360219

Semiconductor manufacturing, characterised by its complex processes, demands efficient anomaly detection (AD) systems for quality assurance. This study extends from previous work utilising unsupervised convolutional autoencoders for AD in Semiconductor batch manufacturing by applying the technique to a novel dataset supplied by a local Semiconductor Manufacturer. Our method uses an approach that employs 1-dimensional convolutional autoencoders (1d-CAE) to improve AD performance and interpretability through the numerical decomposition of reconstruction errors. Identifying anomalies this way allows engineering resources to explain anomalies more effectively than traditional methods. We validate our approach with experiments, demonstrating its performance in accurately detecting anomalies while providing insights into the nature of these irregularities. The experiments also demonstrate the impact of training setup on detection capability, outlining an efficient framework for determining an optimal hyperparameter set-up in an industrial dataset. The proposed unsupervised learning approach with AE reconstruction error improves model explainability, which is expected to be beneficial for deployment in semiconductor manufacturing, where interpretable and trustworthy results are critical for solution adoption by process engineering teams.

关键词： Anomaly Detection Semiconductor Manufacturing convolutional autoencoder Explainable Reconstruction Error

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