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Supervised deep learning embeddings for the prediction of cervical cancer diagnosis

PEERJ COMPUTER SCIENCE 2018年第5期4卷 e154页

作者： Fernandes, Kelwin Chicco, Davide Cardoso, Jaime S. Fernandes, Jessica INESC TEC Inst Engn Sistemas & Comp Tecnol & Ciencia Porto Portugal Univ Porto Porto Portugal Princess Margaret Canc Ctr Toronto ON Canada Univ Cent Venezuela Caracas Venezuela

Cervical cancer remains a significant cause of mortality all around the world, even if it can be prevented and cured by removing affected tissues in early stages. Providing universal and efficient access to cervical screening programs is a challenge that requires identifying vulnerable individuals in the population, among other steps. In this work, we present a computationally automated strategy for predicting the outcome of the patient biopsy, given risk patterns from individual medical records. We propose a machine learning technique that allows a joint and fully supervised optimization of dimensionality reduction and classification models. We also build a model able to highlight relevant properties in the low dimensional space, to ease the classification of patients. We instantiated the proposed approach with deep learning architectures, and achieved accurate prediction results (top area under the curve AUC = 0.6875) which outperform previously developed methods, such as denoising autoencoders. Additionally, we explored some clinical findings from the embedding spaces, and we validated them through the medical literature, making them reliable for physicians and biomedical researchers.

关键词： Dimensionality reduction Health-care informatics denoising autoencoder autoencoder Biomedical informatics Binary classification Deep learning Cervical cancer Artificial neural networks Health informatics

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Unsupervised Sequential Outlier Detection With Deep Architectures

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IEEE TRANSACTIONS ON IMAGE PROCESSING 2017年第9期26卷 4321-4330页

作者： Lu, Weining Cheng, Yu Xiao, Cao Chang, Shiyu Huang, Shuai Liang, Bin Huang, Thomas Tsinghua Univ Dept Automat Beijing 100084 Peoples R China IBM TJ Watson Res Ctr Yorktown Hts NY 10562 USA Univ Washington Dept Ind & Syst Engn Seattle WA 98105 USA Univ Illinois Beckman Inst Urbana IL 61801 USA

Unsupervised outlier detection is a vital task and has high impact on a wide variety of applications domains, such as image analysis and video surveillance. It also gains longstanding attentions and has been extensively studied in multiple research areas. Detecting and taking action on outliers as quickly as possible are imperative in order to protect network and related stakeholders or to maintain the reliability of critical systems. However, outlier detection is difficult due to the one class nature and challenges in feature construction. Sequential anomaly detection is even harder with more challenges from temporal correlation in data, as well as the presence of noise and high dimensionality. In this paper, we introduce a novel deep structured framework to solve the challenging sequential outlier detection problem. We use autoencoder models to capture the intrinsic difference between outliers and normal instances and integrate the models to recurrent neural networks that allow the learning to make use of previous context as well as make the learners more robust to warp along the time axis. Furthermore, we propose to use a layerwise training procedure, which significantly simplifies the training procedure and hence helps achieve efficient and scalable training. In addition, we investigate a fine-tuning step to update all parameters set by incorporating the temporal correlation in the sequence. We further apply our proposed models to conduct systematic experiments on five real-world benchmark data sets. Experimental results demonstrate the effectiveness of our model, compared with other state-of-the-art approaches.

关键词： Sequential anomaly detection deep learning denoising autoencoder recurrent neural networks

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Application of stack marginalised sparse denoising auto-encoder in fault diagnosis of rolling bearing

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JOURNAL OF ENGINEERING-JOE 2018年第16期2018卷 1772-1777页

作者： Zhang, Junling Chen, Zhigang Du, Xiaolei Xu, Xu Yu, Miao Beijing Univ Civil Engn & Architecture Inst Elect & Mech & Vehicle Engn Beijing 100044 Peoples R China Construct Safety Monitoring Engn Technol Res Ctr Beijing 100037 Peoples R China

When a fracturing vehicle is working, it generally needs to bear high loads, media corrosion and erosion. For this special working environment, this study proposes a rolling bearing fault diagnosis method based on stack marginalised sparse denoising auto-encoder (SDAE). This method combines the sparse auto-encoder (SAE) and the denoising auto-encoder (DAE) and combines the characteristics of dimensionality reduction and robustness. The method adds marginalisation to optimise the SDAE. Finally, it uses a two-layer stacking method. The output results of the second marginalised SDAE are used as input to the softmax classifier for learning training and classification testing. This improved method (stack SDAE) improves the denoising ability, reduces the computational complexity, solves the problems of difficult parameter adjustment and slows training convergence. The experimental tests were carried out on the failure of pitting corrosion of the outer ring of the bearing, pitting failure of the inner ring, and cracking of the rolling element. The results show that the algorithm can effectively improve the accuracy of fault diagnosis of rolling bearings, and it has greatly improved than the algorithms of SAEs and DAE.

关键词： rolling bearings fault diagnosis mechanical engineering computing computational complexity corrosion failure analysis cracks fracture signal denoising media corrosion erosion rolling bearing fault diagnosis method two-layer stacking method SDAE stack marginalised sparse denoising autoencoder application fracturing vehicle sparse autoencoder SAE denoising autoencoder DAE softmax classifier learning training classification testing computational complexity pitting corrosion failure cracking

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Biomedical ontology alignment: an approach based on representation learning

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JOURNAL OF BIOMEDICAL SEMANTICS 2018年第1期9卷 21页

作者： Kolyvakis, Prodromos Kalousis, Alexandros Smith, Barry Kiritsis, Dimitris Ecole Polytech Fed Lausanne Route Cantonale CH-1015 Lausanne Switzerland Univ Appl Sci HES SO Western Switzerland Carouge Business Informat Dept Lausanne Switzerland SUNY Buffalo Dept Philosophy 104 Pk Hall Buffalo NY 14260 USA SUNY Buffalo Dept Biomed Informat 104 Pk Hall Buffalo NY 14260 USA

Background: While representation learning techniques have shown great promise in application to a number of different NLP tasks, they have had little impact on the problem of ontology matching. Unlike past work that has focused on feature engineering, we present a novel representation learning approach that is tailored to the ontology matching task. Our approach is based on embedding ontological terms in a high-dimensional Euclidean space. This embedding is derived on the basis of a novel phrase retrofitting strategy through which semantic similarity information becomes inscribed onto fields of pre-trained word vectors. The resulting framework also incorporates a novel outlier detection mechanism based on a denoising autoencoder that is shown to improve performance. Results: An ontology matching system derived using the proposed framework achieved an F-score of 94% on an alignment scenario involving the Adult Mouse Anatomical Dictionary and the Foundational Model of Anatomy ontology (FMA) as targets. This compares favorably with the best performing systems on the Ontology Alignment Evaluation Initiative anatomy challenge. We performed additional experiments on aligning FMA to NCI Thesaurus and to SNOMED CT based on a reference alignment extracted from the UMLS Metathesaurus. Our system obtained overall F-scores of 93.2% and 89.2% for these experiments, thus achieving state-of-the-art results. Conclusions: Our proposed representation learning approach leverages terminological embeddings to capture semantic similarity. Our results provide evidence that the approach produces embeddings that are especially well tailored to the ontology matching task, demonstrating a novel pathway for the problem.

关键词： Ontology matching Semantic similarity Sentence embeddings Word embeddings denoising autoencoder Outlier detection

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Open Set Recognition by Regularising Classifier with Fake Data Generated by Generative Adversarial Networks

Open Set Recognition by Regularising Classifier with Fake Da...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Inhyuk Jo Jungtaek Kim Hyohyeong Kang Yong-Deok Kim Seungjin Choi Department of Computer Science and Engineering POSTECH Korea Software R&D Center Device Solutions Samsung Electronics Korea

ISBN: (纸本)9781538646595

We present a new method to generate fake data in unknown classes in generative adversarial networks (GANs) framework. The generator in GANs is trained to generate somewhat similar to data in known classes but the different one by modelling noisy distribution on feature space of a classifier using proposed marginal denoising autoencoder. The generated data are treated as fake instances in unknown classes and given to the classifier to make it be robust to the real unknown classes. Our results show that synthetic data can act as fake unknown classes and keep down the certainty of the classifier on real unknown classes meanwhile the classification capability of known classes is not degenerated, even improved.

关键词： Generative adversarial networks denoising autoencoder Open set recognition Feature matching Classifiers Unknown Open set Eigenvector synthetic data Classes Network

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ROBUST RECOGNITION OF SPEECH WITH BACKGROUND MUSIC IN ACOUSTICALLY UNDER-RESOURCED SCENARIOS

ROBUST RECOGNITION OF SPEECH WITH BACKGROUND MUSIC IN ACOUST...

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IEEE International Conference on Acoustics, Speech and Signal Processing

作者： Jiri Malek Jindrich Zdansky Petr Cerva Faculty of Mechatronics Informatics and Interdisciplinary Studies Technical University of Liberec Studentská 2 461 17 Liberec Czech Republic

ISBN: (纸本)9781538646595

This paper addresses the task of Automatic Speech Recognition (ASR) with music in the background. We consider two different situations: 1) scenarios with very small amount of labeled training utterances (duration 1 hour) and 2) scenarios with large amount of labeled training utterances (duration 132 hours). In these situations, we aim to achieve robust recognition. To this end we investigate the following techniques: a) multi-condition training of the acoustic model, b) denoising autoencoders for feature enhancement and c) joint training of both above mentioned techniques. We demonstrate that the considered methods can be successfully trained with the small amount of labeled acoustic data. We present substantially improved performance compared to acoustic models trained on clean speech. Further, we show a significant increase of accuracy in the under-resourced scenario, when utilizing additional amount of non-labeled data. Here, the non-labeled dataset is used to improve the accuracy of the feature enhancement via autoencoders. Subsequently, the autoencoders are jointly fine-tuned along with the acoustic model using the small amount of labeled utterances.

关键词： robust speech recognition feature enhancement denoising autoencoder multi-condition training joint training Acoustic models Speech recognition Speech Multiple signal classification Functional training

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Medical image denoising using convolutional denoising autoencoders 16

Medical image denoising using convolutional denoising autoen...

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16th IEEE International Conference on Data Mining (ICDM)

作者： Gondara, Lovedeep Simon Fraser Univ Dept Comp Sci Burnaby BC Canada

ISBN: (纸本)9781509059102

Image denoising is an important pre-processing step in medical image analysis. Different algorithms have been proposed in past three decades with varying denoising performances. More recently, having outperformed all conventional methods, deep learning based models have shown a great promise. These methods are however limited for requirement of large training sample size and high computational costs. In this paper we show that using small sample size, denoising autoencoders constructed using convolutional layers can be used for efficient denoising of medical images. Heterogeneous images can be combined to boost sample size for increased denoising performance. Simplest of networks can reconstruct images with corruption levels so high that noise and signal are not differentiable to human eye.

关键词： Image denoising denoising autoencoder convolutional autoencoder

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Single channel speech enhancement using convolutional neural network

Single channel speech enhancement using convolutional neural...

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IEEE International Workshop of Electronics Control Measurement Signals and their Application to Mechatronics (ECMSM)

作者： Kounovsky, Tomas Malek, Jiri Tech Univ Liberec Fac Mechatron Inst Informat Technol & Elect Liberec Czech Republic

ISBN: (纸本)9781509055821

Neural networks can be used to identify and remove noise from noisy speech spectrum (denoisisng autoencoders, DAEs). The DAEs are typically implemented using the fully-connected feed-forward topology. Usually one of the following possibilities is used as DA target: 1) Ideal frequency ratio mask, which is applied to noisy spectrum to estimate the clean speech spectrum (masking) or 2) Clean speech spectrum directly (mapping). Recent research in the area of automatic speech recognition shows that convolutional neural networks are very promising in speech modeling. In this paper we thus suggest, construct the DAs with the convolutional topology. We investigate the suitability of both above described target types and compare the results with the fully connected DAs. Our experiments indicate that mapping-based convolutional networks estimating log-power spectra achieve significant improvement over all competing topologies and target types. Performance gains of 8% in PESQ scores over the ideal ratio masking are observed. We also investigate the ability of DAEs to enhance speech of unseen language based on the language diversity of the training set. Our experiments suggest that training DAEs with language-diverse training sets does not yield any significant benefit for the task of speech enhancement.

关键词： denoising autoencoder Single channel speech enhancement Convolutional neural network Multilingual training

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ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC

ROBUST AUTOMATIC RECOGNITION OF SPEECH WITH BACKGROUND MUSIC

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

作者： Malek, Jiri Zdansky, Jindrich Cerva, Petr Tech Univ Liberec Fac Mechatron Informat & Interdisciplinary Studie Studentska 2 Liberec 46117 Czech Republic

ISBN: (纸本)9781509041176

This paper addresses the task of Automatic Speech Recognition (ASR) with music in the background, where the accuracy of recognition may deteriorate significantly. To improve the robustness of ASR in this task, e.g. for broadcast news transcription or subtitles creation, we adopt two approaches: 1) multi-condition training of the acoustic models and 2) denoising autoencoders followed by acoustic model training on the preprocessed data. In the latter case, two types of autoencoders are considered: the fully connected and the convolutional network. Presented experimental results show that all the investigated techniques are able to improve the recognition of speech distorted by music significantly. For example, in the case of artificial mixtures of speech and electronic music (low Signal-to-Noise Ratio (SNR) of 0 dB), we achieved absolute improvement of accuracy by 35.8 %. For real-world broadcast news and a high SNR (about 1 0 dB), we achieved improvement by 2 : 4 %. The important advantage of the studied approaches is that they do not deteriorate the accuracy in scenarios with clean speech (the decrease is about 1%).

关键词： Robust recognition background music feature enhancement denoising autoencoder multi-condition training

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autoencoder based Domain Adaptation for Speaker Recognition under Insufficient Channel Information 18

Autoencoder based Domain Adaptation for Speaker Recognition ...

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18th Annual Conference of the International-Speech-Communication-Association (INTERSPEECH 2017)

作者： Shon, Suwon Mun, Seongkyu Kim, Wooil Ko, Hanseok Korea Univ Sch Elect Engn Seoul South Korea Korea Univ Dept Visual Informat Proc Seoul South Korea Incheon Natl Univ Dept Comp Sci & Engn Incheon South Korea

ISBN: (纸本)9781510848764

In real-life conditions, mismatch between development and test domain degrades speaker recognition performance. To solve the issue, many researchers explored domain adaptation approaches using matched in-domain dataset. However, adaptation would be not effective if the dataset is insufficient to estimate channel variability of the domain. In this paper, we explore the problem of performance degradation under such a situation of insufficient channel information. In order to exploit limited in-domain dataset effectively, we propose an unsupervised domain adaptation approach using autoencoder based Domain Adaptation (AEDA). The proposed approach combines an autoencoder with a denoising autoencoder to adapt resource-rich development dataset to test domain. The proposed technique is evaluated on the Domain Adaptation Challenge 13 experimental protocols that is widely used in speaker recognition for domain mismatched condition. The results show significant improvements over baselines and results from other prior studies.

关键词： unsupervised domain adaptation domain mismatch speaker recognition autoencoder denoising autoencoder

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