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Online Self-distillation and Self-modeling for 3D Brain Tumor Segmentation

IEEE Journal of Biomedical and Health Informatics 2025年 PP卷 PP页

作者： Pang, Yan Li, Yunhao Huang, Teng Liang, Jiaming Wang, Zhen Dong, Changyu Kuang, Dongyang Hu, Ying Chen, Hao Lei, Tim Wang, Qiong The Guangdong Provincial Key Laboratory of Computer Vision and Virtual Reality Technology Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China The School of Artificial Intelligence Guangzhou University China The Zhejiang Lab Hangzhou China Sun Yat-sen University China The Department of Computer Science and Engineering The Department of Chemical and Biological Engineering Hong Kong University of Science and Technology China The Department of Electrical Engineering University of Colorado Denver United States

In the specialized domain of brain tumor segmentation, supervised segmentation approaches are hindered by the limited availability of high-quality labeled data, a condition arising from data privacy concerns, significant costs, and ethical issues. In response to this challenge, this paper presents a training framework that adeptly integrates a plug-and-play component, MOD, into current supervised learning models, boosting their efficacy in scenarios with limited data. The MOD consists of an Online Tokenizer and a Dense Predictor, which employs self-distillation and self-modeling on masked patches, promoting swift convergence and efficient representation learning. During the inference phase, the plug-and-play MOD component is excluded, preserving the computational efficiency of the original model without incurring extra processing costs. We substantiated the value of our approach through experiments on leading 3D brain tumor segmentation baselines. Remarkably, models augmented with the MOD consistently showcased superior results, achieving elevated Dice coefficients and HD95 scores on two datasets: BraTS 2021 and MSD 2019 Task-01 Brain Tumor. Code: https://***/aigzhusmart/MOD © 2013 IEEE.

关键词： Self-supervised learning

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Stochastic Sensitivity Regularized Autoencoder for Robust Feature Learning 21

Stochastic Sensitivity Regularized Autoencoder for Robust Fe...

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21st IEEE International Conference on Cognitive Informatics and Cognitive Computing, ICCI*CC 2022

作者： Zhang, Jianjun Wang, Ting Ng, Wing W. Y. Pedrycz, Witold Kwong, Sam School of Computer Science and Engineering South China University of Technology Guangzhou Guangdong China School of Medicine South China University of Technology Guangzhou First People's Hospital Department of Radiology Guangdong Guangzhou China School of Computer Science and Engineering South China University of Technology Guangdong Provincial Key Lab of Computational Intelligence and Cyberspace Information Guangzhou Guangdong China University of Alberta Department of Electrical and Computer Engineering EdmontonAB Canada Hong Kong City University Department of Computer Science Hong Kong Hong Kong

ISBN: (纸本)9781665490849

We present a new regularized autoencoder for robust feature learning. The regularization, implying stochastic sensitivity, is defined as the sum of entries of the absolute covariance matrix of the output perturbation at each layer of the autoencoder. The advantages of the stochastic sensitivity regularization are two-fold. Firstly, we show that the classical Frobenius norm regularization effectively enforces the network to be insensitive to input perturbation and that the Frobenius norm regularization is a special case of the proposed stochastic sensitivity regularization which enables the proposed method to train an autoencoder for robust feature learning. Secondly, we also show that the stochastic sensitivity regularization attempts to drive the network to learn a set of decorrelated feature maps which removes redundant information and thus improves generalization capabilities. These two properties enable the autoencoder to learn a set of robust and diverse feature maps. Finally, the efficacy and the robustness of the proposed regularization method are confirmed a nd quantified by comparing it against existing regularized auto encoders over a range of tasks. © 2022 IEEE.

关键词： Covariance matrix

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Adversarial attacks and defenses in physiological computing:a systematic review

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National science Open 2023年第1期2卷 62-90页

作者： Dongrui Wu Jiaxin Xu Weili Fang Yi Zhang Liuqing Yang Xiaodong Xu Hanbin Luo Xiang Yu Ministry of Education Key Laboratory of Image Processing and Intelligent Control School of Artificial Intelligence and AutomationHuazhong University of Science and TechnologyWuhan 430074China Zhejiang Lab Hangzhou 311121China School of Civil and Hydraulic Engineering Huazhong University of Science and TechnologyWuhan 430074China College of Public Administration Huazhong University of Science and TechnologyWuhan 430074China Electrical Engineering and Computer Science Department University of MichiganAnn Arbor MI 48109USA School of Management and Sino-European Institute for Intellectual Property Huazhong University of Science and TechnologyWuhan 430074China

Physiological computing uses human physiological data as system inputs in real *** includes,or significantly overlaps with,brain-computer interfaces,affective computing,adaptive automation,health informatics,and physiological signal based *** computing increases the communication bandwidth from the user to the computer,but is also subject to various types of adversarial attacks,in which the attacker deliberately manipulates the training and/or test examples to hijack the machine learning algorithm output,leading to possible user confusion,frustration,injury,or even ***,the vulnerability of physiological computing systems has not been paid enough attention to,and there does not exist a comprehensive review on adversarial attacks to *** study fills this gap,by providing a systematic review on the main research areas of physiological computing,different types of adversarial attacks and their applications to physiological computing,and the corresponding defense *** hope this review will attract more research interests on the vulnerability of physiological computing systems,and more importantly,defense strategies to make them more secure.

关键词： physiological computing brain-computer interfaces health informatics biometrics machine learning adversarial attack

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Enhancing Diversity in Multi-Objective Feature Selection

Enhancing Diversity in Multi-Objective Feature Selection

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Congress on Evolutionary Computation

作者： Sevil Zanjani Miyandoab Shahryar Rahnamayan Azam Asilian Bidgoli Sevda Ebrahimi Masoud Makrehchi Nature-Inspired Computational Intelligence (NICI) Lab Department of Electrical Computer and Software Engineering Ontario Tech University Oshawa ON Canada Department of Engineering Brock University St. Catharines ON Canada Paculty of Science Wilfrid Laurier University Waterloo ON Canada

ISBN: (数字)9798350308365

ISBN: (纸本)9798350308372

Feature selection plays a pivotal role in the data preprocessing and model-building pipeline, significantly en-hancing model performance, interpretability, and resource efficiency across diverse domains. In population-based optimization methods, the generation of diverse individuals holds utmost importance for adequately exploring the problem landscape, particularly in highly multi-modal multi-objective optimization problems. Our study reveals that, in line with findings from sev-eral prior research papers, commonly employed crossover and mutation operations lack the capability to generate high-quality diverse individuals and tend to become confined to limited areas around various local optima. This paper introduces an augmen-tation to the diversity of the population in the well-established multi-objective scheme of the genetic algorithm, NSGA-II. This enhancement is achieved through two key components: the genuine initialization method and the substitution of the worst individuals with new randomly generated individuals as a re-initialization approach in each generation. The proposed multi-objective feature selection method undergoes testing on twelve real-world classification problems, with the number of features ranging from 2,400 to nearly 50,000. The results demonstrate that replacing the last front of the population with an equivalent number of new random individuals generated using the genuine initialization method and featuring a limited number of features substantially improves the population's quality and, consequently, enhances the performance of the multi-objective algorithm.

关键词： Sociology Pipelines Optimization methods Evolutionary computation Feature extraction Data models Distance measurement

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Direct Shooting Method for Numerical Optimal Control: A Modified Transcription Approach

Direct Shooting Method for Numerical Optimal Control: A Modi...

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European Control Conference (ECC)

作者： Jiawei Tang Yuxing Zhong Pengyu Wang Xingzhou Chen Shuang Wu Ling Shi Department of Electronic and Computer Engineering Hong Kong University of Science and Technology Water Bay Hong Kong SAR Department Of Electronic And Electrical Engineering Shenzhen Key Laboratory of Robotics Perception and Intelligence Southern University of Science and Technology Shenzhen China Noah's Ark Lab Huawei

ISBN: (数字)9783907144107

ISBN: (纸本)9798331540920

Direct shooting is an efficient method to solve numerical optimal control. It utilizes the Runge-Kutta scheme to discretize a continuous-time optimal control problem making the problem solvable by nonlinear programming solvers. However, conventional direct shooting raises a contradictory dynamics issue when using an augmented state to handle high-order systems. This paper fills the research gap by considering the direct shooting method for high-order systems. We derive the modified Euler and Runge-Kutta-4 methods to transcribe the system dynamics constraint directly. Additionally, we provide the global error upper bounds of our proposed methods. A set of benchmark optimal control problems shows that our methods provide more accurate solutions than existing approaches.

关键词： Upper bound Accuracy System dynamics Optimal control Europe Programming Benchmark testing

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Image Provenance Analysis via Graph Encoding with Vision Transformer

arXiv

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arXiv 2024年

作者： Zhang, Keyang Kong, Chenqi Wang, Shiqi Rocha, Anderson Li, Haoliang The Department of Electrical Engineering City University of Hong Kong Hong Kong Lab School of Electrical and Electronic Engineering Nanyang Technology University Singapore The Department of Computer Science City University of Hong Kong Hong Kong The Artificial Intelligence Lab. Recod.ai The University of Campinas Campinas13084-851 Brazil

Recent advances in AI-powered image editing tools have significantly lowered the barrier to image modification, raising pressing security concerns those related to spreading misinformation and disinformation on social platforms. Image provenance analysis is crucial in this context, as it identifies relevant images within a database and constructs a relationship graph by mining hidden manipulation and transformation cues, thereby providing concrete evidence chains. This paper introduces a novel end-to-end deep learning framework designed to explore the structural information of provenance graphs. Our proposed method distinguishes from previous approaches in two main ways. First, unlike earlier methods that rely on prior knowledge and have limited generalizability, our framework relies upon a patch attention mechanism to capture image provenance clues for local manipulations and global transformations, thereby enhancing graph construction performance. Second, while previous methods primarily focus on identifying tampering traces only between image pairs, they often overlook the hidden information embedded in the topology of the provenance graph. Our approach aligns the model training objectives with the final graph construction task, incorporating the overall structural information of the graph into the training process. We integrate graph structure information with the attention mechanism, enabling precise determination of the direction of transformation. Experimental results show the superiority of the proposed method over previous approaches, underscoring its effectiveness in addressing the challenges of image provenance analysis. Copyright © 2024, The Authors. All rights reserved.

关键词： computer forensics

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The Power of Bamboo: On the Post-Compromise Security for Searchable Symmetric Encryption 30

The Power of Bamboo: On the Post-Compromise Security for Sea...

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30th Annual Network and distributed System Security Symposium, NDSS 2023

作者： Chen, Tianyang Xu, Peng Picek, Stjepan Luo, Bo Susilo, Willy Jin, Hai Liang, Kaitai National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab China Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering China Cluster and Grid Computing Lab School of Computer Science and Technology China Huazhong University of Science and Technology Wuhan430074 China Digital Security Group Radboud University Nijmegen Netherlands Department of EECS Institute of Information Sciences The University of Kansas LawrenceKS United States Institute of Cybersecurity and Cryptology School of Computing and Information Technology University of Wollongong WollongongNSW2522 Australia Faculty of Electrical Engineering Mathematics and Computer Science Delft University of Technology Delft2628 CD Netherlands

ISBN: (纸本)1891562835

Dynamic searchable symmetric encryption (DSSE) enables users to delegate the keyword search over dynamically updated encrypted databases to an honest-but-curious server without losing keyword privacy. This paper studies a new and practical security risk to DSSE, namely, secret key compromise (e.g., a user’s secret key is leaked or stolen), which threatens all the security guarantees offered by existing DSSE schemes. To address this open problem, we introduce the notion of searchable encryption with key-update (SEKU) that provides users with the option of non-interactive key updates. We further define the notion of post-compromise secure with respect to leakage functions to study whether DSSE schemes can still provide data security after the client’s secret key is compromised. We demonstrate that post-compromise security is achievable with a proposed protocol called "Bamboo". Interestingly, the leakage functions of Bamboo satisfy the requirements for both forward and backward security. We conduct a performance evaluation of Bamboo using a real-world dataset and compare its runtime efficiency with the existing forward-and-backward secure DSSE schemes. The result shows that Bamboo provides strong security with better or comparable performance. © 2023 30th Annual Network and distributed System Security Symposium, NDSS 2023. All Rights Reserved.

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Focal volume optics for composite structuring in transparent solids

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International Journal of Extreme Manufacturing 2025年第1期7卷 357-366页

作者： Bo Zhang Zhuo Wang Dezhi Tan Min Gu Yuanzheng Yue Jianrong Qiu State Key Laboratory of Modern Optical Instrumentation College of Optical Science and EngineeringZhejiang UniversityHangzhou 310027People’s Republic of China Zhejiang Lab Hangzhou 311100People’s Republic of China School of Materials Science and Engineering Zhejiang UniversityHangzhou 310027People’s Republic of China Institute of Photonic Chips University of Shanghai for Science and TechnologyShanghaiPeople’s Republic of China Centre for Artificial-Intelligence Nanophotonics School of Optical-Electrical and Computer EngineeringUniversity of Shanghai for Science and TechnologyShanghaiPeople’s Republic of China Department of Chemistry and Bioscience Aalborg University9220 AalborgDenmark

Achieving high-level integration of composite micro-nano structures with different structural characteristics through a minimalist and universal process has long been the goal pursued by advanced manufacturing research but is rarely explored due to the absence of instructive ***,we revealed a controllable ultrafast laser-induced focal volume light field and experimentally succeeded in highly efficient one-step composite structuring in multiple transparent solids.A pair of spatially coupled twin periodic structures reflecting light distribution in the focal volume are simultaneously created and independently tuned by engineering ultrafast laser-matter *** demonstrated that the generated composite micro-nano structures are applicable to multi-dimensional information integration,nonlinear diffractive elements,and multi-functional optical *** work presents the experimental verification of highly universal all-optical fabrication of composite micro-nano structures with independent controllability in multiple degrees of freedom,expands the current cognition of ultrafast laser-based material modification in transparent solids,and establishes a new scientific aspect of strong-field optics,namely,focal volume optics for composite structuring transparent solids.

关键词： ultrafast laser focal volume light field composite structuring transparent solids advanced manufacturing

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Active Learning with the nnUNet and Sample Selection with Uncertainty-Aware Submodular Mutual Information Measure 7

Active Learning with the nnUNet and Sample Selection with Un...

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7th International Conference on Medical Imaging with Deep Learning, MIDL 2024

作者： Föllmer, Bernhard Schulze, Kenrick Wald, Christian Stober, Sebastian Samek, Wojciech Dewey, Marc Department of Radiology Charité-Universitätsmedizin Berlin Freie Universität Berlin Humboldt-Universität zu Berlin Berlin10117 Germany Institute of Mathematics Technical University of Berlin Berlin Germany Artificial Intelligence Lab. Otto-von-Guericke-Universität Magdeburg Germany Department of Artificial Intelligence Fraunhofer Heinrich Hertz Institute Berlin Germany BIFOLD – Berlin Institute for the Foundations of Learning and Data Berlin Germany Department of Electrical Engineering and Computer Science Technical University of Berlin Berlin Germany partner site Berlin Berlin Germany

Annotating medical images for segmentation tasks is a time-consuming process that requires expert knowledge. Active learning can reduce this annotation cost and achieve optimal model performance by selecting only the most informative samples for annotation. However, the effectiveness of active learning sample selection strategies depends on the model architecture and training procedure used. The nnUNet has achieved impressive results in various automated medical image segmentation tasks due to its self-configuring pipeline for automated model design and training. This raises the question of whether the nnUNet is applicable in an active learning setting to avoid cumbersome manual configuration of the training process and improve accessibility for non-experts in deep learning-based segmentation. This paper compares various sample selection strategies in an active learning setting in which the self-configuring nnUNet is used as the segmentation model. Additionally, we propose a new sample selection strategy for UNet-like architectures: USIM - Uncertainty-Aware Submodular Mutual Information Measure. The method combines uncertainty and submodular mutual information to select batches of uncertain, diverse, and representative samples. We evaluate the performance gain and labeling costs on three medical image segmentation tasks with different segmentation challenges. Our findings demonstrate that utilizing nnUNet as the segmentation model in an active learning setting is feasible, and most sampling strategies outperform random sampling. Furthermore, we demonstrate that our proposed method yields a significant improvement compared to existing baseline methods. © 2024 CC-BY 4.0, B. Föllmer, K. Schulze, C. Wald, S. Stober, W. Samek & M. Dewey.

关键词： Active learning

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Highsimb: A Concrete Blockchain High Simulation with Contract Vulnerability Detection for Ethereum and Hyperledger Fabric 4th

Highsimb: A Concrete Blockchain High Simulation with Contra...

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4th International Conference on Machine Learning for Cyber Security, ML4CS 2022

作者： Huang, Pengfei Jie, Wanqing Voundi Koe, Arthur Sandor Hou, Ruitao Yan, Hongyang Nouioua, Mourad Thien, Phan Duc Mbous Ikong, Jacques Lancine, Camara Institute of Artificial Intelligence and Blockchain Guangzhou University Guangzhou510006 China Pazhou Lab Guangzhou510330 China Faculty of Mathematics and Computer Science University of Mohamed Bachir El Ibrahimi Bordj Bou Arreridj34030 Algeria Faculty of Information Technology Nam Dinh University of Technology Education Nam Dinh420000 Viet Nam Department of Electrical and Telecommunications Engineering National Advanced School of Engineering - University of Yaounde I Yaounde337 Cameroon Department of Computer Science University of Social Sciences and Management Bamako2575 Mali

ISBN: (纸本)9783031200984

Blockchain testing plays a critical role in the maturation of blockchain technology by ensuring the quality of implemented functional and non-functional requirements. In the new global economy, rapid time to market has become a central issue: developers fail to scrutinize their blockchain designs prior to deployment and customers undergo negative experiences that hurt the widespread adoption of the blockchain technology. Previous published studies aimed for effective blockchain simulators. However, existing solutions exhibit several drawbacks: they rely on guesswork, conceal low-level implementation details, lack expected realistic outcomes and automated testing, as well as lag in smart contract vulnerability analysis. In this paper, we introduce highsimb: the first concrete blockchain high simulation platform for Ethereum and Hyperledger Fabric that supports smart contract vulnerability detection. Unlike a testnet, the blockchain tester can customize any low-level detail to achieve realistic expected results under automated testing. Theoretical analysis demonstrates our concrete simulator is highly observable, supports realistic feedback, is scalable, detects smart contract vulnerabilities, has strong white-box testing capabilities and automates experiments. Our framework complements existing blockchain simulators and introduces a novel development paradigm for blockchain testing. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Blockchain

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