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Deep learning for brain age estimation: A systematic review

Information Fusion 2023年 96卷 130-143页

作者： Tanveer, M. Ganaie, M.A. Beheshti, Iman Goel, Tripti Ahmad, Nehal Lai, Kuan-Ting Huang, Kaizhu Zhang, Yu-Dong Del Ser, Javier Lin, Chin-Teng Department of Mathematics Indian Institute of Technology Indore Simrol Indore453552 India Department of Robotics University of Michigan Ann ArborMI48109 United States Department of Human Anatomy and Cell Science Rady Faculty of Health Sciences Max Rady College of Medicine University of Manitoba WinnipegMB Canada Biomedical Imaging Lab National Institute of Technology Silchar Assam 788010 India Department of Electrical Engineering and Computer Science National Taipei University of Technology Taipei Taiwan Department of Electronic Engineering National Taipei University of Technology Taipei Taiwan Data Science Research Center Duke Kunshan University China School of Computing and Mathematical Sciences University of Leicester LeicesterLE1 7RH United Kingdom Derio48160 Spain Bilbao48013 Spain School of Computer Science Faculty of Engineering and Information Technology University of Technology Sydney Sydney Australia

Over the years, Machine Learning models have been successfully employed on neuroimaging data for accurately predicting brain age. Deviations from the healthy brain aging pattern are associated with the accelerated brain aging and brain abnormalities. Hence, efficient and accurate diagnosis techniques are required to elicit accurate brain age estimations. Several contributions have been reported in the past for this purpose, resorting to different data-driven modeling methods. Recently, deep neural networks (also referred to as deep learning) have become prevalent in manifold neuroimaging studies, including brain age estimation. In this review, we offer a comprehensive analysis of the literature related to the adoption of deep learning for brain age estimation with neuroimaging data. We detail and analyze different deep learning architectures used for this application, pausing at research works published to date quantitatively exploring their application. We also examine different brain age estimation frameworks, comparatively exposing their advantages and weaknesses. Finally, the review concludes with an outlook towards future directions that should be followed by prospective studies. The ultimate goal of this paper is to establish a common and informed reference for newcomers and experienced researchers willing to approach brain age estimation by using deep learning models. © 2023 Elsevier B.V.

关键词： Deep neural networks

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Characterizing Continual Learning Scenarios and Strategies for Audio Analysis

arXiv

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arXiv 2024年

作者： Bhatt, Ruchi Kumari, Pratibha Mahapatra, Dwarikanath Saddik, Abdulmotaleb El Saini, Mukesh The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India The Faculty of Informatics and Data Science University of Regensburg Regensburg Germany Inception Institute of AI Abu Dhabi United Arab Emirates Abu Dhabi United Arab Emirates School of Electrical Engineering and Computer Science University of Ottawa Canada The Department of Computer Science & Engineering Indian Institute of Technology Punjab Ropar India Abu Dhabi United Arab Emirates

Audio analysis is useful in many application scenarios. The state-of-the-art audio analysis approaches assume that the data distribution at training and deployment time will be the same. However, due to various real-life environmental factors, the data may encounter drift in its distribution or can encounter new classes in the late future. Thus, a one-time trained model might not perform adequately. In this paper, we characterize continual learning (CL) approaches in audio analysis. In this paper, we characterize continual learning (CL) approaches, intended to tackle catastrophic forgetting arising due to drifts. As there is no CL dataset for audio analysis, we use DCASE 2020 to 2023 datasets to create various CL scenarios for audio-based monitoring tasks. We have investigated the following CL and non-CL approaches: EWC, LwF, SI, GEM, A-GEM, GDumb, Replay, Naive, cumulative, and joint training. The study is very beneficial for researchers and practitioners working in the area of audio analysis for developing adaptive models. We observed that Replay achieved better results than other methods in the DCASE challenge data. It achieved an accuracy of 70.12% for the domain incremental scenario and an accuracy of 96.98% for the class incremental scenario. Copyright © 2024, The Authors. All rights reserved.

关键词： Machine learning

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An Image Semantic Communication System Based on GAN and Relative Position-Encoding

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IEEE Communications Letters 2025年第6期29卷 1280-1284页

作者： Zhang, Ziqi Tan, Weijie Li, Chunguo Guizhou University College of Big Data and Information Engineering Guiyang550025 China Guizhou University State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang550025 China Advanced Cryptography and System Security Key Laboratory of Sichuan Province Chengdu610025 China Ministry of Industry and Information Technology Laboratory of Cryptography Application Technology Innovation and Testing Verification Beijing100015 China Southeast University School of Information Science and Engineering Nanjing210096 China

In image semantic communication, the complex wireless channel environment leads to the loss of image details and performance degradation during transmission. To address this issue, we propose an image semantic communication system based on Generative Adversarial Networks (GAN) and Relative Position-Encoding (RPE), named GAN-RpeSC. Specifically, in the system design, we introduce the GAN network at the output of the semantic communication framework to enhance the image restoration. We integrate the RPE module into the Visual Transformer (ViT) module, adding the RPE module to its Multihead Self Attention (MHSA), so that the model can better capture the spatial location information of the image. Experimental results show that the proposed GAN-RpeSC has significant advantages in improving image transmission quality, with a 0.3 dB improvement in peak signal-to-noise ratio (PSNR) compared to the ViT module alone. © 1997-2012 IEEE.

关键词： Encoding (symbols)

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One-to-Multiple Clean-Label Image Camouflage (Omclic) Based Backdoor Attack on Deep Learning

SSRN

引用

SSRN 2023年

作者： Wang, Guohong Ma, Hua Gao, Yansong Abuadbba, Alsharif Zhang, Zhi Wei, Kang Al-Sarawi, Said F. Zhang, Gongxuan Abbott, Derek School of Cyber Science and Engineering Nanjing University of Science and Technology Jiangsu Nanjing China School of Electrical and Electronic Engineering The University of Adelaide Australia Department of Computer Science and Software Engineering The University of Western Australia Australia Data61 Commonwealth Scientific and Industrial Research Organisation CSIRO Australia

Image camouflage has been utilized to create clean-label poisoned images for implanting backdoor into a DL model. But there exists a crucial limitation that one attack/poisoned image can only fit a single input size of the DL model, which greatly increases its attack budget when attacking multiple commonly adopted input sizes of DL models. This work proposes to constructively craft an attack image through camouflaging but can fit multiple DL models’ input sizes simultaneously, namely OmClic. Thus, through OmClic, we are able to always implant a backdoor regardless of which common input size is chosen by the user to train the DL model given the same attack budget (i.e., a fraction of the poisoning rate). With our camouflaging algorithm formulated as a multi-objective optimization, M = 5 input sizes can be concurrently targeted with one attack image, which artifact is retained to be almost visually imperceptible at the same time. Extensive evaluations validate the proposed OmClic can reliably succeed in various settings using diverse types of images. Further experiments on OmClic based backdoor insertion to DL models show that high backdoor performances (i.e., attack success rate and clean data accuracy) are achievable no matter which common input size is randomly chosen by the user to train the model. So that the OmClic based backdoor attack budget is reduced by M× compared to the state-of-the-art camouflage based backdoor attack as a baseline. Significantly, the same set of OmClic based poisonous attack images is transferable to different model architectures for backdoor implant. © 2023, The Authors. All rights reserved.

关键词： Deep learning

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Active Geospatial Search for Efficient Tenant Eviction Outreach

arXiv

引用

arXiv 2024年

作者： Sarkar, Anindya DiChristofano, Alex Das, Sanmay Fowler, Patrick J. Jacobs, Nathan Vorobeychik, Yevgeniy Washington University in St. Louis United States George Mason University United States Department of Computer Science and Engineering Division of Computational & Data Sciences Brown School Washington University in St. Louis United States

Tenant evictions threaten housing stability and are a major concern for many cities. An open question concerns whether data-driven methods enhance outreach programs that target at-risk tenants to mitigate their risk of eviction. We propose a novel active geospatial search (AGS) modeling framework for this problem. AGS integrates property-level information in a search policy that identifies a sequence of rental units to canvas to both determine their eviction risk and provide support if needed. We propose a hierarchical reinforcement learning approach to learn a search policy for AGS that scales to large urban areas containing thousands of parcels, balancing exploration and exploitation and accounting for travel costs and a budget constraint. Crucially, the search policy adapts online to newly discovered information about evictions. Evaluation using eviction data for a large urban area demonstrates that the proposed framework and algorithmic approach are considerably more effective at sequentially identifying eviction cases than baseline methods. © 2024, CC BY-NC-SA.

关键词： Budget control

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Automated Detection and Segmentation of Glioblastoma in MRI Using Multi-Level Diffusion Transformer U-Net

Automated Detection and Segmentation of Glioblastoma in MRI ...

引用

IEEE International Symposium on Biomedical Imaging

作者： Anum Masood Usman Naseem Junaid Rashid Imran Razzak Department of Radiology Harvard Medical School Harvard University Boston U.S.A. Institute of Neurosciences and Medicine (INM) Forschungszentrum Juelich Juelich Germany School of Computing Macquarie University Sydney Australia Department of Data Science Sejong University Korea School of Computer Science and Engineering UNSW Sydney Australia

ISBN: (数字)9798350313338

ISBN: (纸本)9798350313345

Detecting malignant tumors at an early stage is crucial for effective treatment and an improved prognosis of brain cancer. Radiologists use MRI imaging modality to detect and diagnose glioblastoma. For efficient detection, numerous deeplearning methods have been proposed in the past decade. Detecting brain tumors in MR images is challenging owing to the heterogeneity in anatomical structures across patients, and difficulties posed by low contrast, inhomogeneous intensity levels, and artifacts. In this paper, we proposed a multi-level Diffusion Transformer U-Net (mDT-UNet) with a modified cross-attention module. Our model extracts features from both global and local levels of the MR images and manual segmentations providing a wider range of contextual information. Our proposed model attained an average accuracy of 95% on the BraTS 2020 dataset whereas U-Net achieved 81% and Swin Transformer achieved 86% accuracy. In addition, we evaluated our model on various benchmark datasets; CPM-RadPath, UCSF PDGM, EGD, and IvyGAP. With appropriate pre-training on large medical datasets, our model can be optimized for various medical image segmentation tasks.

关键词： Image segmentation Accuracy Sensitivity Magnetic resonance imaging Biological system modeling Benchmark testing Transformers

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Machine Fault Diagnosis Using EMD-Gammatone Texture Representation and A Lightweight Self-Attention SqueezeNet

Machine Fault Diagnosis Using EMD-Gammatone Texture Represen...

引用

International Conference on Big data and Smart Computing (BIGCOMP)

作者： Mahe Zabin Anika Nahian Binte Kabir Muhammad Khubayeeb Kabir Ho-Jin Choi Jia Uddin School of Computing Korea Advanced Institute of Science & Technology KAIST Daejeon South Korea Department of Computer Science and Engineering Brac University Dhaka Bangladesh Artificial Intelligence and Big Data Department Woosong University Daejeon South Korea

Machine fault diagnosis involves the intricate process of detecting and isolating faults, which is particularly challenging due to various sources of noise and the complex nature of the faults. In automatic fault diagnosis, feature engineering becomes daunting because of the time-varying characteristics of the fault signals. As artificial intelligence continues to advance, deep learning models have been increasingly employed for machine fault classification. However, a significant limitation of state-of-the-art models is their high computational complexity, making them unsuitable for deployment on portable devices. In this paper, a lightweight fault diagnosis model is proposed that consists of a self-attention SqueezeN et architecture along with a hybrid texture representation technique using empirical mode decomposition (EMD) and gammatone-spectrogram (GS) filter. In the model, initially, the dominant signal is extracted from the ID audio fault signals by discarding lower intrinsic mode functions (IMFs) from EMD and then the dominant signals are converted to 2D texture maps applying the GS filter. Then, the generated texture maps are fed to the modified self-attention SqueezeNet classifier, with reduced model width and depth as input for training and validation. In the experimental evaluation, two public benchmark datasets- ToyADMOS and MIMII are used to validate the model. Finally, the model classifies the machine audio faults using the trained features. The experimental results demonstrated that the hybrid EMD-Gammatone texture imaging outperforms the other state-of-the-art methods like MFCC, Gammatone, and Hilbert Huang Transform with a self-attention (SA) based SqueezeNet architecture. The EMD-Gammatone spectrum-based feature extraction accurately detected the faults by exhibiting an accuracy of 89.32% and 96.46% for MIMII and ToyADMOS datasets, respectively. In addition, with the EMD-Gammatone Spectrogram images, the proposed model outperforms the conventional

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Robin: A Novel Method to Produce Robust Interpreters for Deep Learning-Based Code Classifiers

Robin: A Novel Method to Produce Robust Interpreters for Dee...

引用

IEEE International Conference on Automated Software engineering (ASE)

作者： Zhen Li Ruqian Zhang Deqing Zou Ning Wang Yating Li Shouhuai Xu Chen Chen Hai Jin School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan China Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security Cluster and Grid Computing Lab National Engineering Research Center for Big Data Technology and System Hubei Engineering Research Center on Big Data Security Department of Computer Science University of Colorado Colorado Springs USA Center for Research in Computer Vision University of Central Florida USA School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China

Deep learning has been widely used in source code classification tasks, such as code classification according to their functionalities, code authorship attribution, and vulnerability detection. Unfortunately, the black-box nature of deep learning makes it hard to interpret and understand why a classifier (i.e., classification model) makes a particular prediction on a given example. This lack of interpretability (or explainability) might have hindered their adoption by practitioners because it is not clear when they should or should not trust a classifier's prediction. The lack of interpretability has motivated a number of studies in recent years. However, existing methods are neither robust nor able to cope with out-of-distribution examples. In this paper, we propose a novel method to produce Robust interpreters for a given deep learning-based code classifier; the method is dubbed Robin. The key idea behind Robin is a novel hybrid structure combining an interpreter and two approximators, while leveraging the ideas of adversarial training and data augmentation. Experimental results show that on average the interpreter produced by Robin achieves a 6.11% higher fidelity (evaluated on the classifier), 67.22% higher fidelity (evaluated on the approximator), and 15.87x higher robustness than that of the three existing interpreters we evaluated. Moreover, the interpreter is 47.31% less affected by out-of-distribution examples than that of LEMNA.

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An Improved Conflict Evidence Management Approach Using Base Belief Function for Uncertain Prior Information Modeling

An Improved Conflict Evidence Management Approach Using Base...

引用

Chinese Control Conference (CCC)

作者： Yongchuan Tang Yonghao Zhou Ying Zhou Shuang Ni Yubo Huang Deyun Zhou School of Microelectronics Northwestern Polytechnical University Xian Shaanxi China School of Computer Science Northwestern Polytechnical University Xian Shaanxi China School of Electronic Information Northwestern Polytechnical University Xian Shaanxi China School of Big Data and Software Engineering Chongqing University Chongqing China School of Engineering University of Warwick Coventry United Kingdom

Due to the complexity in practical environment, there are a variety of interference resulting in inaccurate information which will directly affect the final result of data fusion. In the era of big data, as the number of fusing information sources increases, even if they are consistent, conflicts may arise. However, classical Dempster combination rule in Dempster Shafer evidence theory (D-S theory) cannot solve the problem of conflict data fusion. Therefore, an improved method is proposed for conflict data fusion by assigning a base belief to each piece of evidence. In this paper, the base belief function is used to construct the initial belief degree firstly. Then, the belief entropy is calculated to get the information volume of each evidence. Dempster combination rule is used to get the final result after evidence modification, which can help solve conflict data fusion better. Numerical examples and experiments are used to verity the rationality and effectiveness of the proposed method.

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Dual Contrastive Learning: Text Classification via Label-Aware data Augmentation

arXiv

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arXiv 2022年

作者： Chen, Qianben Zhang, Richong Zheng, Yaowei Mao, Yongyi SKLSDE School of Computer Science and Engineering Beihang University Beijing China Beijing Advanced Institution on Big Data and Brain Computing Beihang University Beijing China School of Electrical Engineering and Computer Science University of Ottawa Ottawa Canada

Contrastive learning has achieved remarkable success in representation learning via self-supervision in unsupervised settings. However, effectively adapting contrastive learning to supervised learning tasks remains as a challenge in practice. In this work, we introduce a dual contrastive learning (DualCL) framework that simultaneously learns the features of input samples and the parameters of classifiers in the same space. Specifically, DualCL regards the parameters of the classifiers as augmented samples associating to different labels and then exploits the contrastive learning between the input samples and the augmented samples. Empirical studies on five benchmark text classification datasets and their low-resource version demonstrate the improvement in classification accuracy and confirm the capability of learning discriminative representations of DualCL. Copyright © 2022, The Authors. All rights reserved.

关键词： Text processing

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