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Reliable Central Nervous System Tumor Differentiation on MRI Images with Deep Neural Networks and Conformal Prediction 1

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Reliable Central Nervous System Tumor Differentiation on MRI...

1st Workshop on Explainable Artificial Intelligence for the Medical Domain, EXPLIMED 2024

作者： Balderas, Luis de Castro, María Moreno Lastra, Miguel Martínez, Jose P. Pérez, Francisco J. Laínez, Antonio Arauzo-Azofra, Antonio Benítez, José M. School of Sciences Technology and Engineering’s Doctorates Department of Computer Science and Artificial Intelligence DiCITS DaSCI iMUDS University of Granada Granada18071 Spain Department of Software Engineering DiCITS DaSCI iMUDS University of Granada Granada18071 Spain Radiodiagnosis Service Hospital Universitario "Virgen de las Nieves" Granada Spain ibs.Granada: Instituto Biosanitario de Granada Granada Spain Rural Engineering Department DiCITS Lab University of Córdoba Córdoba14005 Spain

Central nervous system tumors, particularly gliomas, rank among the top 10 causes of cancer-related deaths worldwide. Thus, precise differentiation of these tumors is crucial for effective treatment, which can reduce patient suffering and lower mortality rates. We propose a deep learning-based technique for glioma differentiation using MRI imaging, which incorporates two novel approaches. First, we represent perfusion sequences as time series, which serve as inputs for a Deep Neural Network (DNN) classifier. This classifier is trained to distinguish the sequences between low-grade glioma (LGG) and high-grade glioma (HGG). Second, we employ Conformal Prediction to calibrate the results, ensuring they include the true category with a 90% probability. This approach has been rigorously tested to evaluate its performance. Our experimental results demonstrate that our deep neural network not only provides accurate but also ensures trustworthy predictions. © 2024 Copyright © for this paper by its authors.

关键词： Lung cancer

A Privacy Preserving computer-aided Medical Diagnosis Framework with Outsourced Model

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A Privacy Preserving Computer-aided Medical Diagnosis Framew...

IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Xinyu Feng Qingni Shen Cong Li Xuewei Wang Niantao Xie Luyuan Xie Yuejian Fang Zhonghai Wu School of Software and Microelectronics Peking University Beijing China National Engineering Research Center for Software Engineering Peking University Beijing China PKU-OCTA Laboratory for Blockchain and Privacy Computing Peking University Beijing China School of Computer Science Peking University Beijing China

computer-aided diagnosis plays an increasingly important role in modern medical activities, relying largely on the deployment of medical machine learning models. Protecting the security of model parameters is crucial for model providers. However, the current schemes for protecting model parameters are mostly interactive. This interactive nature makes it difficult to support offline deployment of models and flexible authorization of prediction results, thus hindering the widespread application of computer-aided diagnosis. To address these limitations, we propose a new computer-aided medical diagnosis framework by designing a new identity-based inner product functional proxy re-encryption (IB-IPFPRE) scheme. Our framework supports private deployment of medical diagnostic models without compromising model parameters. It also enables access control of prediction results based on user identity. Compared to existing privacy-preserving prediction techniques, our framework significantly reduces communication overhead and does not require the model owner to be online in real-time. Furthermore, our scheme enables flexible delegation of prediction results, allowing users to authorize the sharing of prediction results with other entities as needed. We conducted extensive experiments for logistic regression on three medical datasets. The experiments demonstrate that our scheme achieved 40% to 7× performance improvement in LAN environment and 13× to 15× improvement in WAN environment, and did not require any communication overhead during the privacy preserving prediction phase.

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Wavelet-based Global-Local Interaction Network with Cross-Attention for Multi-View Diabetic Retinopathy Detection

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arXiv 2025年

作者： Hu, Yongting Lin, Yuxin Liu, Chengliang Luo, Xiaoling Dou, Xiaoyan Xu, Qihao Xu, Yong School of Computer Science and Technology Harbin Institute of Technology Shenzhen Shenzhen China Shenzhen Key Laboratory of Visual Object Detection and Recognition Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China Ophthalmology Department Shenzhen Second People’s Hospital Shenzhen China

Multi-view diabetic retinopathy (DR) detection has recently emerged as a promising method to address the issue of incomplete lesions faced by single-view DR. However, it is still challenging due to the variable sizes and scattered locations of lesions. Furthermore, existing multi-view DR methods typically merge multiple views without considering the correlations and redundancies of lesion information across them. Therefore, we propose a novel method to overcome the challenges of difficult lesion information learning and inadequate multi-view fusion. Specifically, we introduce a two-branch network to obtain both local lesion features and their global dependencies. The high-frequency component of the wavelet transform is used to exploit lesion edge information, which is then enhanced by global semantic to facilitate difficult lesion learning. Additionally, we present a cross-view fusion module to improve multi-view fusion and reduce redundancy. Experimental results on large public datasets demonstrate the effectiveness of our method. The code is open sourced on https://***/HuYongting/WGLIN. Copyright © 2025, The Authors. All rights reserved.

关键词： Wavelet transforms

A Performance Investigation of Genetic Algorithm with Different Evolutionary Operators

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A Performance Investigation of Genetic Algorithm with Differ...

Machine Intelligence Theory and Applications (MiTA), International Conference on

作者： Jian-Yu Li Zhao-Kun Shao Zi-Jia Wang Yuhui Zhang Ruoyu Zhu Zong-Gan Chen Tao Li College of Artificial Intelligence Nankai University Tianjin China College of Electrical and Electronic Engineering Hanyang University Ansan Korea College of Software Engineering Guangzhou University Guanzhou China College of Computer Science and Technology Dongguan University of Technology Dongguan China School of Computer Science South China Normal University Guangzhou China College of Computer and Information Engineering Henan Normal University Xinxiang China

ISBN: (数字)9798350385670

ISBN: (纸本)9798350385687

Genetic Algorithm (GA) as a bio-inspired optimization technique, has shown significant potential for applications in many fields. This paper aims to provide a comprehensive overview of the main types of evolutionary operators in GA—selection, crossover, and mutation, which have a direct and significant influence on optimization performance. First, this study systematically summarizes the various evolutionary operators currently used in the field of GA. Subsequently, these different operators are embedded into the standard GA framework to assess their influence on algorithm performance. Last, this paper also explores the effect of setting crossover and mutation rates on GA performance, determining the optimal values of these parameters through a series of experiments. This research not only offers a comprehensive overview of the evolutionary operators in GA but also demonstrates the specific performance of different operator combinations through empirical evidence, providing valuable reference for further research and application of GA.

关键词： Refining Focusing Space exploration Optimization Standards Machine intelligence Genetic operators Convergence

Ranking Aggregation with Interactive Feedback for Collaborative Person Re-identification 33

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Ranking Aggregation with Interactive Feedback for Collaborat...

33rd British Machine Vision Conference Proceedings, BMVC 2022

作者： Huang, Ji Liang, Chao Zhang, Yue Wang, Zhongyuan Zhang, Chunjie National Engineering Research Center for Multimedia Software School of Computer Science Wuhan University Wuhan430072 China Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University Wuhan430072 China Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing Jiaotong University Beijing100044 China

Person re-identification (re-ID) aims to retrieve the same person from a group of networking cameras. Ranking aggregation (RA), a method to aggregates multiple ranking results, can further improve the retrieval accuracy in re-ID tasks. Existing RA work can be generally divided into unsupervised methods and fully-supervised methods. Unsupervised methods lack external supervision, can hardly achieve the optimal results. In contrast, fully-supervised methods need massive labeling data for training, which is prohibitively expensive in the practical application. This paper studies interactive RA (IRA) to address the above challenges in existing RA research. The core idea is to utilize a small amount of supervisory information, obtained from users' relevance feedback, to supervise RA method to produce better re-ranking results. Compared with unsupervised methods, IRA introduces supervisory information and thus has better aggregation accuracy. Compared with fully-supervision methods, the supervisory information of IRA is more readily available, and can be targeted to specific queries. Particularly, we propose two IRA implementations, based on ranking positions and scores respectively, to adapt to diverse application scenarios, where rankers only give rankings, or rankers give similarity scores. Experiments on three public re-ID datasets have shown that IRA significantly outperforms the-state-of-art unsupervised baselines, and achieves similar accuracy with less labeling cost than the fully-supervised RA method. © 2022. The copyright of this document resides with its authors. It may be distributed unchanged freely in print or electronic forms.

关键词： computer vision

Exploring the Role of Large Language Models in Cybersecurity: A Systematic Survey

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arXiv 2025年

作者： Tian, Shuang Zhang, Tao Liu, Jiqiang Wang, Jiacheng Wu, Xuangou Zhu, Xiaoqiang Zhang, Ruichen Zhang, Weiting Yuan, Zhenhui Mao, Shiwen Kim, Dong In School of Software Engineering Beijing Jiaotong University Beijing100044 China School of Cyberspace Science and Technology Beijing Jiaotong University Beijing100044 China School of Computer Science and Engineering Nanyang Technological University Singapore639798 Singapore School of Computer Science and Technology An hui Province Key Laboratory of Digital Twin Technology in Metallurgical Industry Anhui University of Technology Anhui243002 China College of Computing and Data Science Nanyang Technological University Singapore School of Electronic and Information Engineering Beijing Jiaotong University Beijing100044 China School of Engineering University of Warwick CoventryCV4 7AL United Kingdom Department of Electrical and Computer Engineering Auburn University AuburnAL36849-5201 United States Department of Electrical and Computer Engineering College of Information and Communication Engineering Sungkyunkwan University Suwon16419 Korea Republic of

—With the rapid development of technology and the acceleration of digitalisation, the frequency and complexity of cyber security threats are increasing. Traditional cybersecurity approaches, often based on static rules and predefined scenarios, are struggling to adapt to the rapidly evolving nature of modern cyberattacks. There is an urgent need for more adaptive and intelligent defence strategies. The emergence of Large Language Model (LLM) provides an innovative solution to cope with the increasingly severe cyber threats, and its potential in analysing complex attack patterns, predicting threats and assisting real-time response has attracted a lot of attention in the field of cybersecurity, and exploring how to effectively use LLM to defend against cyberattacks has become a hot topic in the current research field. This survey examines the applications of LLM from the perspective of the cyber attack lifecycle, focusing on the three phases of defense reconnaissance, foothold establishment, and lateral movement, and it analyzes the potential of LLMs in Cyber Threat Intelligence (CTI) tasks. Meanwhile, we investigate how LLM-based security solutions are deployed and applied in different network scenarios. It also summarizes the internal and external risk issues faced by LLM during its application. Finally, this survey also points out the facing risk issues and possible future research directions in this domain. Copyright © 2025, The Authors. All rights reserved.

关键词： Cyber attacks

Learning from Imperfect Data: Towards Efficient Knowledge Distillation of Autoregressive Language Models for Text-to-SQL

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arXiv 2024年

作者： Zhong, Qihuang Chen, Kunfeng Ding, Liang Liu, Juhua Du, Bo Tao, Dacheng School of Computer Science National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence Hubei Key Laboratory of Multimedia and Network Communication Engineering Wuhan University China The University of Sydney Australia Nanyang Technological University Singapore

Large Language Models (LLMs) have shown promising performance in text-to-SQL, which involves translating natural language questions into SQL queries. However, current text-to-SQL LLMs are computationally expensive and challenging to deploy in real-world applications, highlighting the importance of compressing them. To achieve this goal, knowledge distillation (KD) is a common approach, which aims to distill the larger teacher model into a smaller student model. While numerous KD methods for autoregressive LLMs have emerged recently, it is still under-explored whether they work well in complex text-to-SQL scenarios. To this end, we conduct a series of analyses and reveal that these KD methods generally fall short in balancing performance and efficiency. In response to this problem, we propose to improve the KD with Imperfect Data, namely KID, which effectively boosts the performance without introducing much training budget. The core of KID is to efficiently mitigate the training-inference mismatch by simulating the cascading effect1 of inference in the imperfect training data. Extensive experiments on 5 text-to-SQL benchmarks show that, KID can not only achieve consistent and significant performance gains (up to +5.83% average score) across all model types and sizes, but also effectively improve the training efficiency. Copyright © 2024, The Authors. All rights reserved.

关键词： Structured Query Language

PERSON RETRIEVAL IN PHYSICAL WORLD

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PERSON RETRIEVAL IN PHYSICAL WORLD

2021 IEEE International Conference on Multimedia and Expo, ICME 2021

作者： Huang, Wenxin Li, Dongyang Hu, Ruimin Liang, Chao Zhong, Xian School of Computer and Information Engineering Hubei University China National Engineering Research Center for Multimedia Software Wuhan University China Research Institute of Wuhan University Shenzhen China School of Computer Science and Technology Wuhan University of Technology China

ISBN: (纸本)9781665438643

Person re-identification (re-ID) gains plenty of achievements as a retrieval problem in constrained camera networks. However, most of the researches are concentrated on visual appearance, they still suffer from the complicated environments in unconstrained urban/campus surveillance scenario due to unreliable visual representations with extremely challenging problems as lack of training samples, amounts of irrelevant crowds, etc. Besides, most of the existing person re-ID datasets neglect the physical truth of realistic investigation application: 1) investigators search only few suspects among amounts of crowds. Moreover, he may not go through by every camera in the surveillance area and may appear in the same camera several times;and 2) the corresponding characteristic in multi-space of the same ID can be verified with each other. Therefore, we propose a person retrieval in physical world (PRPW) dataset with large-scale unconstrained surveillance scenario. It contains over 1.4 million bounding boxes, including 20 labeled IDs and numerous irrelevant crowds captured by 86 cameras. Furthermore, over 30,000 records of 20 mobile trajectories are collected in this dataset, and the 20 mobile trajectories are partially overlapped while passing by 86 cameras. Finally, based on two common senses and a verification experiment, we provide a proposal to tackle with PRPW task on the basis of trajectory association which utilizes global optimization to compensate for the errors caused by visual expression on local observation points. The comparison experiments with two typical unsupervised person re-ID methods are implemented on the constructed dataset. © 2021 IEEE

关键词： Cameras

GPUSCAN++: Efficient Structural Graph Clustering on GPUs

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arXiv 2023年

作者： Yuan, Long Zhou, Zeyu Lin, Xuemin Chen, Zi Zhao, Xiang Zhang, Fan The School of Computer Science and Engineering Nanjing University of Science and Technology Nanjing China The School of Computer Science and Engineering The University of New South Wales Sydney Australia The Software Engineering Institute East China Normal University Shanghai China The College of Systems Engineering National University of Defense Technology Changsha China Cyberspace Institute of Advanced Technology Guangzhou University Guangzhou China

Structural clustering is one of the most popular graph clustering methods, which has achieved great performance improvement by utilizing GPUs. Even though, the state-of-the-art GPU-based structural clustering algorithm, GPUSCAN, still suffers from efficiency issues since lots of extra costs are introduced for parallelization. Moreover, GPUSCAN assumes that the graph is resident in the GPU memory. However, the GPU memory capacity is limited currently while many real-world graphs are big and cannot fit in the GPU memory, which makes GPUSCAN unable to handle large graphs. Motivated by this, we present a new GPU-based structural clustering algorithm, GPUSCAN++, in this paper. To address the efficiency issue, we propose a new progressive clustering method tailored for GPUs that not only avoid high parallelization costs but also fully exploits the computing resources of GPUs. To address the GPU memory limitation issue, we propose a partition-based algorithm for structural clustering that can process large graphs with limited GPU memory. We conduct experiments on real graphs, and the experimental results demonstrate that our algorithm can achieve up to 168 times speedup compared with the state-of-the-art GPU-based algorithm when the graph can be resident in the GPU memory. Moreover, our algorithm is scalable to handle large graphs. As an example, our algorithm can finish the structural clustering on a graph with 1.8 billion edges using less than 2 GB GPU memory. Copyright © 2023, The Authors. All rights reserved.

关键词： Graphics processing unit