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EGFR Assessment in Lung Cancer CT Images: Analysis of Local and Holistic Regions of Interest Using Deep Unsupervised Transfer Learning

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IEEE ACCESS 2021年 9卷 58667-58676页

作者： Silva, Francisco Pereira, Tania Morgado, Joana Frade, Julieta Mendes, Jose Freitas, Claudia Negrao, Eduardo De Lima, Beatriz Flor Silva, Miguel Correia Da Madureira, Antonio J. Ramos, Isabel Hespanhol, Venceslau Costa, Jose Luis Cunha, Antonio Oliveira, Helder P. Inst Syst & Comp Engn Technol & Sci INESC TEC P-4200465 Porto Portugal Univ Porto FEUP Fac Engn P-4200465 Porto Portugal Univ Porto FCUP Fac Sci P-4169007 Porto Portugal Ctr Hosp & Univ Sao Joao CHUSJ P-4200319 Porto Portugal Univ Porto FMUP Fac Med P-4200319 Porto Portugal Univ Porto Inst Invest & Inovacao Saude i3S P-4200135 Porto Portugal Univ Porto IPATIMUP Inst Mol Pathol & Immunol P-4200135 Porto Portugal Univ Tras Os Montes & Alto Douro UTAD P-5001801 Vila Real Portugal

Statistics have demonstrated that one of the main factors responsible for the high mortality rate related to lung cancer is the late diagnosis. Precision medicine practices have shown advances in the individualized treatment according to the genetic profile of each patient, providing better control on cancer response. Medical imaging offers valuable information with an extensive perspective of the cancer, opening opportunities to explore the imaging manifestations associated with the tumor genotype in a non-invasive way. This work aims to study the relevance of physiological features captured from Computed Tomography images, using three different 2D regions of interest to assess the Epidermal growth factor receptor (EGFR) mutation status: nodule, lung containing the main nodule, and both lungs. A convolutional autoencoder was developed for the reconstruction of the input image. Thereafter, the encoder block was used as a feature extractor, stacking a classifier on top to assess the EGFR mutation status. Results showed that extending the analysis beyond the local nodule allowed the capture of more relevant information, suggesting the presence of useful biomarkers using the lung with nodule region of interest, which allowed to obtain the best prediction ability. This comparative study represents an innovative approach for gene mutations status assessment, contributing to the discussion on the extent of pathological phenomena associated with cancer development, and its contribution to more accurate Artificial Intelligence-based solutions, and constituting, to the best of our knowledge, the first deep learning approach that explores a comprehensive analysis for the EGFR mutation status classification.

关键词： Computed tomography Lung Feature extraction Lung cancer Transfer learning Databases Tumors convolutional autoencoder EGFR prediction lung cancer transfer learning unsupervised feature learning

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CSI-Based Human Activity Recognition Using Multi-Input Multi-Output autoencoder and Fine-Tuning

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SENSORS 2023年第7期23卷 3591-3591页

作者： Chahoushi, Mahnaz Nabati, Mohammad Asvadi, Reza Ghorashi, Seyed Ali Shahid Beheshti Univ Fac Elect Engn Dept Telecommun Cognit Telecommun Res Grp Tehran *** Iran Univ East London Sch Architecture Comp & Engn Dept Comp Sci & Digital Technol London E16 2RD England

Wi-Fi-based human activity recognition (HAR) has gained considerable attention recently due to its ease of use and the availability of its infrastructures and sensors. Channel state information (CSI) captures how Wi-Fi signals are transmitted through the environment. Using channel state information of the received signals transmitted from Wi-Fi access points, human activity can be recognized with more accuracy compared with the received signal strength indicator (RSSI). However, in many scenarios and applications, there is a serious limit in the volume of training data because of cost, time, or resource constraints. In this study, multiple deep learning models have been trained for HAR to achieve an acceptable accuracy level while using less training data compared to other machine learning techniques. To do so, a pretrained encoder which is trained using only a limited number of data samples, is utilized for feature extraction. Then, by using fine-tuning, this encoder is utilized in the classifier, which is trained by a fraction of the rest of the data, and the training is continued alongside the rest of the classifier's layers. Simulation results show that by using only 50% of the training data, there is a 20% improvement compared with the case where the encoder is not used. We also showed that by using an untrainable encoder, an accuracy improvement of 11% using 50% of the training data is achievable with a lower complexity level.

关键词： channel state information (CSI) convolutional autoencoder human activity recognition (HAR) machine learning (ML)

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Unsupervised learning framework for temperature compensated damage identification and localization in ultrasonic guided wave SHM with transfer learning

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ULTRASONICS 2023年第1期130卷 106931页

作者： Sawant, Shruti Sethi, Amit Banerjee, Sauvik Tallur, Siddharth Indian Inst Technol Dept Civil Engn CE Mumbai 400076 Maharashtra India Indian Inst Technol Dept Elect Engn EE Mumbai 400076 Maharashtra India

Damage localization algorithms for ultrasonic guided wave-based structural health monitoring (GW-SHM) typically utilize manually-defined features and supervised machine learning on data collected under various conditions. This scheme has limitations that affect prediction accuracy in practical settings when the model encounters data with a distribution different from that used for training, especially due to variation in envi-ronmental factors (e.g., temperature) and types of damages. While deep learning based models that overcome these limitations have been reported in literature, they typically comprise of millions of trainable parameters. As an alternative, we propose an unsupervised approach for temperature-compensated damage identification and localization in GW-SHM systems based on transferring learning from a convolutional auto encoder (TL -CAE). Remarkably, without using signals corresponding to the damages during training (unsupervised), our method demonstrates more accurate damage detection and localization as well as robustness to temperature variations than supervised approaches reported on the publicly available Open Guided Waves (OGW) dataset. Additionally, we have demonstrated reduction in number of trainable parameters using transfer learning (TL) to leverage similarities between time-series among various sensor paths. It is also worth noting that the proposed framework uses raw time-domain signals, without any pre-processing or knowledge of material properties. It should therefore be scalable and adaptable for other materials, structures, damages, and temperature ranges, should more data become available in the future. We present an extensive parametric study to demonstrate feasibility of the proposed method.

关键词： convolutional autoencoder Transfer learning (TL) Unsupervised temperature compensation Structural health monitoring (SHM) Ultrasonic guided wave Damage localization

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autoencoder: Issues, Challenges and Future Prospect 3rd

Autoencoder: Issues, Challenges and Future Prospect

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3rd International Conference on Recent Innovations and Technological Development in Mechanical Engineering, ICRITDME 2020

作者： Maheshwari, Anega Mitra, Priyanka Sharma, Bhavna Department of Computer Science Jaipur Engineering College and Research Centre Rajasthan Jaipur India

ISBN: (纸本)9789811692352

As of more recently, deep learning-based models have demonstrated considerable potential, as they have outperformed all traditional practices. When data becomes high dimensional, extraction of features and compression of data become progressively significant. In this paper, we describe the autoencoder deep learning algorithm. autoencoder is primarily a neural network-based feature extraction methodology that accomplishes outstanding victory in producing highlights of high-dimensional data. autoencoder assumes a principal job in unsupervised learning which targets to rework inputs into outputs with minimal reconstruction error. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： autoencoder convolutional autoencoder Denoising autoencoder Unsupervised learning

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A novel lifelong learning model based on cross domain knowledge extraction and transfer to classify underwater images

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INFORMATION SCIENCES 2021年 552卷 80-101页

作者： Irfan, Muhammad Zheng Jiangbin Iqbal, Muhammad Arif, Muhammad Hassan Northwestern Polytech Univ Sch Software Xian Peoples R China Higher Coll Technol Fac Comp & Informat Sci Fujairah U Arab Emirates CESAT Islamabad Pakistan

Artificial intelligence based autonomous systems interacting with dynamic environment are required to continuously learn, accumulate and improve the learned knowledge. Currently, most artificial intelligence based systems lack this ability and work in isolated learning paradigm. Human beings follow the continuous learning process by retaining and accumulating the learnt knowledge, and by using the learnt knowledge to solve the problem at hand. In this paper, we present a lifelong learning model, to solve challenging problem of real world underwater image classification. The proposed model is capable to learn from simple problems, accumulates the learnt knowledge by continual learning and uses the learnt knowledge to solve future complex problems of the same or related domain, in a similar way as humans do. In the proposed model, firstly, a deep classification convolutional autoencoder is presented to extract spatially localized features from images by utilizing convolution filters, then a code fragment based learning classifier system, with rich knowledge encoding scheme, is proposed for knowledge representation and transfer. In order to validate the model, experiments are conducted on two underwater images datasets and one in-air images dataset. Experiments results demonstrate that the proposed method outperforms base line method and state-of-the-art convolution neural network (CNN) methods. (C) 2020 Elsevier Inc. All rights reserved.

关键词： Lifelong learning Underwater image classification Learning classifier systems convolutional autoencoder

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Deep-learning-based guided wave detection for liquid-level state in porcelain bushing type terminal

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STRUCTURAL CONTROL & HEALTH MONITORING 2021年第1期28卷 e2651-e2651页

作者： Hong, Xiaobin Zhang, Bin Liu, Yuan Qi, Hongchang Li, Weihua South China Univ Technol Sch Mech & Automot Engn Guangzhou 510640 Peoples R China Guangzhou Power Supply Bur Transmiss Management Inst Guangzhou Peoples R China

The structural security of civil energy equipment is significant for the steady operation of power supply system, and porcelain bushing type terminal is a typical energy equipment that needs long-term monitoring. As a nondestructive structural health monitoring method, ultrasonic guided wave (UGW) technology is extremely suitable for state detection of energy equipment. However, most current UGW methods still need to manually select the guided wave features, which rely heavily on the guidance of expert experience. This article presents a deep-learning method to directly utilize original-guided wave signals to quantitatively detect the liquid-level state. Firstly, the original signals were fed into convolutional autoencoder (CAE) to catch the low-dimension representation and realize the automatic feature extraction. Then, the low-dimension representations were orderly input into the long short-term memory (LSTM) recurrent neural network for liquid-level regression. In feature extraction step, CAE method can effectively extract the useful features and remove the interference and signal distortion. In regression step, both the accuracy and the robustness of proposed method are better than backpropagation network and convolutional neural network. Experimental results show that proposed CAE-LSTM method can accurately inspect the liquid level by original signals and implement maintenance monitoring.

关键词： convolutional autoencoder deep learning guided wave LSTM PZT

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Tracking sustainable development in mining towns: A novel framework integrating socioeconomic and eco-environmental perspectives through coupling coordination degree

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ENVIRONMENTAL IMPACT ASSESSMENT REVIEW 2024年 109卷

作者： Sun, Xiaofei Shao, Huaiyong Liang, Shuneng Zhou, Yingzhi Dai, Xiaoai Liu, Meng Tao, Ran Guo, Zhiheng Xin, Qi Chengdu Univ Technol Coll Geog & Planning Chengdu 610059 Peoples R China Chengdu Univ Technol State Key Lab Geohazard Prevent & Geoenvironm Prot Chengdu 610059 Peoples R China Minist Educ Key Lab Earth Explorat & Informat Technol Chengdu 610059 Peoples R China Minist Nat Resources China Land Satellite Remote Sensing Applicat Ctr Beijing 100048 Peoples R China Chinese Acad Agr Sci Inst Agr Resources & Reg Planning Key Lab Agr Remote Sensing Minist Agr & Rural Affairs Beijing 100081 Peoples R China Sichuan Forestry & Grassland Bur Forest & Grassland Fire Monitoring Ctr Sichuan Pro Chengdu 610081 Peoples R China China Southwest Architectural Design & Res Inst CO Urban & Ind Planning Res Ctr Chengdu 610042 Peoples R China Southwest Univ Sci & Technol Sch Environm & Resource Mianyang 621010 Peoples R China

Assessing coupling coordination (CCD) between socioeconomic development (SD) and eco-environmental quality (EQ) is vital for sustainable development blueprints for mining towns. However, previous studies often overlooked the policy context and the asymmetry of SD and EQ impacts on CCD, in the modeling process, leading to a lack of objectivity and generalizability in the current methods. Therefore, this study devised a novel framework for assessing the CCD in mining towns by leveraging convolutional autoencoders and the Cusp Catastrophe model. The effectiveness of the framework was verified using Panxi mining town in China as a case study. Results demonstrate: (1) 65% primary coupling coordination indicates lagging SD in the Panxi mining towns in 2020;(2) there is an evident exponential growth trend in CCD of the Panxi mining towns grew exponentially (R2 = 0.94) from -0.351 in 2001 to 0.062 in 2020, and transforming from disorder to primary coupling coordination in 2015;(3) post-2013, effective local policies and measures boosted CCD by 35%, but currently they have not been continuously transformed into a driving force for CCD growth. Therefore, considering the practical challenges associated with different coupling coordination categories, this study provides recommendations for sustainable development at the township scale. The results provides insights for Panxi's sustainable development and aids in developing reliable predictive models for CCD for other mining towns.

关键词： Coupling coordination Spatial modeling convolutional autoencoder Cusp catastrophe model Mining towns Sustainability

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Dual Residual Denoising autoencoder with Channel Attention Mechanism for Modulation of Signals

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SENSORS 2023年第2期23卷 1023-1023页

作者： Duan, Ruifeng Chen, Ziyu Zhang, Haiyan Wang, Xu Meng, Wei Sun, Guodong Beijing Forestry Univ Sch Informat Sci & Technol Beijing 100083 Peoples R China Natl Forestry & Grassland Adm Engn Res Ctr Forestry Oriented Intelligent Informa Beijing 100083 Peoples R China

Aiming to address the problems of the high bit error rate (BER) of demodulation or low classification accuracy of modulation signals with a low signal-to-noise ratio (SNR), we propose a double-residual denoising autoencoder method with a channel attention mechanism, referred to as DRdA-CA, to improve the SNR of modulation signals. The proposed DRdA-CA consists of an encoding module and a decoding module. A squeeze-and-excitation (SE) ResNet module containing one residual connection is modified and then introduced into the autoencoder as the channel attention mechanism, to better extract the characteristics of the modulation signals and reduce the computational complexity of the model. Moreover, the other residual connection is further added inside the encoding and decoding modules to optimize the network degradation problem, which is beneficial for fully exploiting the multi-level features of modulation signals and improving the reconstruction quality of the signal. The ablation experiments prove that both the improved SE module and dual residual connections in the proposed method play an important role in improving the denoising performance. The subsequent experimental results show that the proposed DRdA-CA significantly improves the SNR values of eight modulation types in the range of -12 dB to 8 dB. Especially for 16QAM and 64QAM, the SNR is improved by 8.38 dB and 8.27 dB on average, respectively. Compared to the DnCNN denoising method, the proposed DRdA-CA makes the average classification accuracy increase by 67.59 similar to 74.94% over the entire SNR range. When it comes to the demodulation, compared with the RLS and the DnCNN denoising algorithms, the proposed denoising method reduces the BER of 16QAM by an average of 63.5% and 40.5%, and reduces the BER of 64QAM by an average of 46.7% and 18.6%. The above results show that the proposed DRdA-CA achieves the optimal noise reduction effect.

关键词： signal denoising convolutional autoencoder channel attention residual connection AWGN

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Machine learning-based prediction of operation conditions from plasma plume images of atmospheric-pressure plasma reactors

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JOURNAL OF PHYSICS D-APPLIED PHYSICS 2023年第25期56卷 254002-254002页

作者： Bong, Cheolwoo Kim, Byeong Soo Ali, Mohammed H. A. Kim, Dongju Bak, Moon Soo Sungkyunkwan Univ Sch Mech Engn Suwon South Korea Sungkyunkwan Univ Dept Smart Fab Technol Suwon South Korea

A technique was proposed in this paper to monitor the key operating conditions of a plasma abatement system, which are the concentration of the carbon-containing process gas and the treatment flowrate, from a plasma plume image acquired using an inexpensive color camera. The technique is based on the observation that the shape and color of the plasma plume vary with the variations in the specific energy input and plasma gas composition. In addition, because these variations are marginal and it is challenging to identify an analytical relationship between these variations and the operating conditions, the prediction model is obtained in a data-driven manner. Specifically, the model was composed of a set of convolutional autoencoders (CAEs) and a dense neural network. Furthermore, it was trained only with images captured under normal operation so that (1) images captured under abnormal operations could be identified based on the reconstruction error of the trained CAEs and (2) predictions are made only on normal images. As a demonstration, methane was tested as a process gas, and oxygen was used as a reaction agent in a nitrogen-rich environment. The test results showed that the optimized model could predict the treatment flowrate and process gas concentration with 96% probability within +/- 3.08 slpm and +/- 300 ppm, respectively.

关键词： plasma abatement plasma monitoring anomaly detection convolutional autoencoder gradient-weighted activation mapping

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Generative Deep Learning-Based Thermographic Inspection of Artwork

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SENSORS 2023年第14期23卷 6362页

作者： Liu, Yi Wang, Fumin Jiang, Zhili Sfarra, Stefano Liu, Kaixin Yao, Yuan Zhejiang Univ Technol Inst Proc Equipment & Control Engn Hangzhou 310023 Peoples R China Univ Aquila Dept Ind & Informat Engn & Econ Piazzale E Pontieri 1 Monteluco Roio I-67100 Laquila Italy North Univ China Shanxi Key Lab Signal Capturing & Proc Taiyuan 030051 Peoples R China Natl Tsing Hua Univ Dept Chem Engn Hsinchu 300044 Taiwan

Infrared thermography is a widely utilized nondestructive testing technique in the field of artwork inspection. However, raw thermograms often suffer from problems, such as limited quantity and high background noise, due to limitations inherent in the acquisition equipment and experimental environment. To overcome these challenges, there is a growing interest in developing thermographic data enhancement methods. In this study, a defect inspection method for artwork based on principal component analysis is proposed, incorporating two distinct deep learning approaches for thermographic data enhancement: spectral normalized generative adversarial network (SNGAN) and convolutional autoencoder (CAE). The SNGAN strategy focuses on augmenting the thermal images, while the CAE strategy emphasizes enhancing their quality. Subsequently, principal component thermography (PCT) is employed to analyze the processed data and improve the detectability of defects. Comparing the results to using PCT alone, the integration of the SNGAN strategy led to a 1.08% enhancement in the signal-to-noise ratio, while the utilization of the CAE strategy resulted in an 8.73% improvement.

关键词： artwork infrared thermography convolutional autoencoder generative adversarial network panel painting

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