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HistoMSC: Density and topology analysis for AI-based visual annotation of histopathology whole slide images

Computers in Biology and Medicine 2025年 190卷 109991-109991页

作者： Ahmad, Zahoor Al-Thelaya, Khaled Alzubaidi, Mahmood Joad, Faaiz Gilal, Nauman Ullah Mifsud, William Boughorbel, Sabri Pintore, Giovanni Gobbetti, Enrico Schneider, Jens Agus, Marco Division of Information and Computing Technology College of Science and Engineering Hamad Bin Khalifa University Qatar Foundation Doha Qatar Kite Pharma Santa MonicaCA United States Visual and Data-intensive Computing CRS4 Italy Qatar Computing Research Institute Hamad Bin Khalifa University Qatar Foundation Doha Qatar

We introduce an end-to-end framework for the automated visual annotation of histopathology whole slide images. Our method integrates deep learning models to achieve precise localization and classification of cell nuclei with spatial data aggregation to extend classes of sparsely distributed nuclei across the entire slide. We introduce a novel and cost-effective approach to localization, leveraging a U-Net architecture and a ResNet-50 backbone. The performance is boosted through color normalization techniques, helping achieve robustness under color variations resulting from diverse scanners and staining reagents. The framework is complemented by a YOLO detection architecture, augmented with generative methods. For classification, we use context patches around each nucleus, fed to various deep architectures. Sparse nuclei-level annotations are then aggregated using kernel density estimation, followed by color-coding and isocontouring. This reduces visual clutter and provides per-pixel probabilities with respect to pathology taxonomies. Finally, we use Morse–Smale theory to generate abstract annotations, highlighting extrema in the density functions and potential spatial interactions in the form of abstract graphs. Thus, our visualization allows for exploration at scales ranging from individual nuclei to the macro-scale. We tested the effectiveness of our framework in an assessment by six pathologists using various neoplastic cases. Our results demonstrate the robustness and usefulness of the proposed framework in aiding histopathologists in their analysis and interpretation of whole slide images. © 2025 The Authors

关键词： Image annotation

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LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

LUOJIA Explorer: An Auto-UAV for Unexposed Space Exploration

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2024 Symposium on Spatial Information to Empower the Metaverse

作者： Sun, Shangzhe Chen, Chi Yang, Bisheng Li, Liuchun Xu, Yuhang Jin, Ang State Key Laboratory of Information Engineering in Surveying Mapping and Remote Sensing Wuhan University Wuhan China Engineering Research Center for Spatio-temporal Data Smart Acquisition and Application Ministry of Education of China China Institute of Artificial Intelligence in Geomatics Wuhan University Wuhan China Institute of Artificial Intelligence School of Computer Science Wuhan University Wuhan China

With the advancement of technology, unexposed spaces have emerged as a new type of strategic area, attracting significant attention from researchers. These spaces often present complex environments, such as extreme lighting variations, irregular geometries, and the lack of external positioning signals, making human entry hazardous. Fortunately, advancements in robotics and artificial intelligence have enabled unmanned systems to mitigate these challenges, allowing for safer exploration of unexposed spaces. UAVs, as vital unmanned system platforms, are extensively used across various industrial scenarios and are particularly effective for detecting unexposed spaces. To facilitate automated exploration of these areas, we propose the autonomous UAV system, LUOJIA Explorer. Equipped with a LiDAR sensor, data transmission module, and external anti-collision devices to protect the aircraft, LUOJIA Explorer is designed for effective unexposed space detection. We have integrated a laser point cloud positioning and mapping subsystem, along with a planning and control subsystem, into the LUOJIA Explorer. Through both simulation and actual experiments, the Luojia Explorer has demonstrated the capability to achieve stable flight based on self-positioning in unexposed spaces, with an exploration efficiency of 88.83 m3/s. © Author(s) 2024.

关键词： Aircraft accidents

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Differential Privacy-based Avatar Update in Cooperative Intelligent Transportation System Metaverse 10

Differential Privacy-based Avatar Update in Cooperative Inte...

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10th International Conference on Computer and Communications, ICCC 2024

作者： Khan, Salabat Luo, Fei Khan, Mansoor Ullah, Insaf Amin, Farhan Ullah, Shamsher Rahim, Mussadiq Abdul School of Computer and Enformation Engineering Qilu Institute of Technology Jinan China School of Computing and Information Technology Great Bay University Dongguan China School of Intelligent Manufacturing and Control Engineering Qilu Institute of Technology Jinan China Institute for Analytics and Data Science University of Essex ColchesterCO4 3SQ United Kingdom School of Computer Science and Engineering Yeungnam University Korea Republic of Shenzhen University National Engineering Laboratory for Big Data Shenzhen China Dhahran31261 Saudi Arabia

Metaverse is the next generation and the successor to the Internet. Various sectors allocated capital to adapt the metaverse due to its inherent importance. Cooperative Intelligent Transportation Systems (C-ITS) is one of the important sectors that can benefit from metaverse adaptation and integration. The C-ITS and metaverse integration give rise to new use cases and cyber attacks. However, traditional Pseudonym Certificate (PCert) authentication based on Vehicular Public Key Infrastructure (VPKI) cannot meet the new use cases and thwart cyber attacks. We present a Differential Privacy (DP)-based PCert and avatar update model to ensure the privacy of end-devices (e.g., vehicles) in the C-ITS metaverse. The proposed DP-based model adds noise to avatar attributes (e.g., color, behavior) to prevent avatar linkage during the PCert update process. Our method ensures that PCerts and avatars cannot be linked to end-devices and the previous PCerts and avatars. We simulate the DP-based method to investigate it in terms of privacy, latency, color distortion, and adversarial success rate, and the results show that DP-based is effective and practical for avatar updates. © 2024 IEEE.

关键词： Advanced driver assistance systems

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Crop Yield Prediction Using Deep Reinforcement Learning 2

Crop Yield Prediction Using Deep Reinforcement Learning

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2nd International Conference on Trends in Electrical, Electronics and Computer Engineering, TEECCON 2023

作者： Deivasigamani, Sudhakar Jaya Mabel Rani, A. Natchadalingam, R. Vijayakarthik, P. Bala Sendhil Kumar, G. Reddy, Pundru Chandra Shaker Digipulse Technologies Inc Scrum Master NJ United States Saveetha Institute of Medical and Technical Sciences Saveetha School of Engineering Tamil Nadu Chennai India R.L.Jalappa Institute of Technology CSE-Data Science Karnataka Bengaluru India R.L.Jalappa Institute of Technology Computer Science and Engineering Karnataka Bengaluru India Sri Manakula Vinayagar Engineering College Master of Business Administration Puducherry India Lovely Professional University School of Computer Science and Engineering Punjab Phagwara India

ISBN: (纸本)9798350339949

Predicting crop production using information about the environment, soil, water, and crops themselves is an area ripe for investigation. Deep learning-based models often extract crop attributes for prediction. These techniques have the following drawbacks: The quality of the extracted characteristics substantially affects the efficacy of those models, and there is no simple non-linear or linear mapping between raw data and crop production estimates. Deep RL guides and inspires to compensate. Deep reinforcement learning uses RL and DL to accurately forecast agricultural yields from raw data. A Deep Recurrent Q-Network model predicts agricultural yield. This model uses Q-Learning reinforcement learning and a Recurrent Neural Network deep learning algorithm. data parameters feed the Recurrent Neural network's layers. Q-learning networks forecast agricultural yields from data. Q-values are linear mappings from Recurrent Neural Network output values. A threshold and parametric features help the reinforcement learning agent predict crop productivity. Finally, the agent is awarded a final score that takes into account the steps taken to both reduce error and improve forecast accuracy. By maintaining the original data distribution, the suggested model efficiently predicts the crop production with an accuracy of 93.7%, exceeding competing models. © 2023 IEEE.

关键词： Crops

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Adaptive Evolutionary Reinforcement Learning Algorithm with Early Termination Strategy 24

Adaptive Evolutionary Reinforcement Learning Algorithm with ...

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Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems

作者： Xiaoqiang Wu Qingling Zhu Qiuzhen Lin Weineng Chen Jianqiang Li College of Computer Science and Software Engineering Shenzhen University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China School of Computer Science and Engineering South China University of Technology Guangzhou China

ISBN: (纸本)9798400704864

Evolutionary reinforcement learning algorithms (ERLs), which combine evolutionary algorithms (EAs) with reinforcement learning (RL), have demonstrated significant success in enhancing RL performance. However, most ERLs rely heavily on Gaussian mutation operators to generate new individuals. When the standard deviation is too large or small, this approach will result in the production of poor or highly similar offspring. Such outcomes can be detrimental to the learning process of the RL agent, as too many poor or similar experiences are generated by these individuals. In order to alleviate these issues, this paper proposes an Adaptive Evolutionary Reinforcement Learning (AERL) method that adaptively adjusts both the standard deviation and the evaluation process. By tracking the performance of new individuals, AERL maintains the mutation strength within a suitable range without the need for additional gradient computations. Moreover, the proposed AERL approach early terminates unnecessary evaluations and discards experiences arising from poor individuals, thereby resulting in enhanced learning efficiency. Empirical assessments conducted on a variety of continuous control problems demonstrate the effectiveness of the AERL method.

关键词： evolutionary algorithm evolutionary reinforcement learning reinforcement learning

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Auto-Attention Mechanism for Multi-View Deep Embedding Clustering

SSRN

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SSRN 2023年

作者： Diallo, Bassoma Hu, Jie Li, Tianrui Khan, Ghufran Ahmad Liang, Xinyan Wang, Hongjun School of Computing and Artificial Intelligence Southwest Jiaotong University Chengdu611756 China Institute of Big Data Science and Industry Shanxi University Shanxi Taiyuan030006 China Department of Computer Science and Application KL University Vaddeswaram522302 India

In several fields, deep learning has achieved tremendous success. Multi-view learning is a workable method for handling data from several sources. For clustering multi-view data, deep learning and multi-view learning are excellent options. However, a persistent challenge is the need for the current deep learning approach to independently drive divergent neural networks for different perspectives while working with data from multiple sources. The current methods have some drawbacks. The first is that a variety of viewpoints are used to calculate neural network statistics. Consequently, as the number of views rises, it results in a considerable calculation. The latter is that it tries vainly to unite various viewpoints at the training. Incorporating a triple fusion technique, this research suggests an innovative multi-view deep embedding clustering (MDEC) model. The suggested model may jointly acquire the specific knowledge in each view as well as the portion information of the collective views. The main goal of the MDEC is to lower the errors made when learning the features of each view and correlating data from many views. To address the optimization problem, MDEC model advises a suitable iterative updating approach. In testing modern deep learning and non-deep learning algorithms, the experimental study on small and large-scale multi-view data shows encouraging results for the MDEC model. In multi-view clustering, this work demonstrates the benefit of the deep learning-based approach over the non-ones. However, future work will address a variety of issues related to MDEC. © 2023, The Authors. All rights reserved.

关键词： Embeddings

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From Knowledge Graph Development to Serving Industrial Knowledge Automation:A Review 41

From Knowledge Graph Development to Serving Industrial Knowl...

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第41届中国控制会议

作者： Guangxuan Song Dongmei Fu Dawei Zhang Key Laboratory of Knowledge Automation for Industrial Processes of Ministry of Education School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Beijing Engineering Research Center of Industrial Spectrum Imaging School of Automation and Electrical EngineeringUniversity of Science and Technology Beijing Shunde Graduate School of University of Science and Technology Beijing Beijing Advanced Innovation Center for Materials Genome Engineering Institute for Advanced Materials and TechnologyUniversity of Science and Technology Beijing National Materials Corrosion and Protection Data Center University of Science and Technology Beijing

ISBN: (数字)9789887581536

ISBN: (纸本)9781665482561

Knowledge is essential for the management,scheduling,and operation of all industrial *** automation refers to the automation of knowledge-based work,which is a disruptive technology of the *** in the era of big data,KG inherits the good interpretability of traditional symbolism system approaches and becomes one of the feasible and important directions to realize industrial knowledge *** paper begins with the origin of KGs,introduces the characteristics of KGs,and reviews the state of the art of KG through the cases of KG constructed and applied in general and vertical *** paper also focuses on the research progress of KG applications in industry,by identifying the current challenges of KG in industrial applications,and highlighting the substantial potential of KG to serve knowledge automation.

关键词： Knowledge Knowledge Graph(KG) Knowledge Automation Symbolism Industry

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Why Does Little Robustness Help? A Further Step Towards Understanding Adversarial Transferability

arXiv

引用

arXiv 2023年

作者： Zhang, Yechao Hu, Shengshan Zhang, Leo Yu Shi, Junyu Li, Minghui Liu, Xiaogeng Wan, Wei Jin, Hai School of Cyber Science and Engineering Huazhong University of Science and Technology China School of Software Engineering Huazhong University of Science and Technology China School of Information and Communication Technology Griffith University Australia School of Computer Science and Technology Huazhong University of Science and Technology China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Hubei Key Laboratory of Distributed System Security China Hubei Engineering Research Center on Big Data Security China Cluster and Grid Computing Lab

Adversarial examples for deep neural networks (DNNs) have been shown to be transferable: examples that successfully fool one white-box surrogate model can also deceive other black-box models with different architectures. Although a bunch of empirical studies have provided guidance on generating highly transferable adversarial examples, many of these findings fail to be well explained and even lead to confusing or inconsistent advice for practical use. In this paper, we take a further step towards understanding adversarial transferability, with a particular focus on surrogate aspects. Starting from the intriguing "little robustness" phenomenon, where models adversarially trained with mildly perturbed adversarial samples can serve as better surrogates for transfer attacks, we attribute it to a trade-off between two dominant factors: model smoothness and gradient similarity. Our research focuses on their joint effects on transferability, rather than demonstrating the separate relationships alone. Through a combination of theoretical and empirical analyses, we hypothesize that the data distribution shift induced by off-manifold samples in adversarial training is the reason that impairs gradient similarity. Building on these insights, we further explore the impacts of prevalent data augmentation and gradient regularization on transferability and analyze how the trade-off manifest in various training methods, thus building a comprehensive blueprint for the regulation mechanisms behind transferability. Finally, we provide a general route for constructing superior surrogates to boost transferability, which optimizes both model smoothness and gradient similarity simultaneously, e.g., the combination of input gradient regularization and sharpness-aware minimization (SAM), validated by extensive experiments. In summary, we call for attention to the united impacts of these two factors for launching effective transfer attacks, rather than optimizing one while ignoring the other,

关键词： Deep neural networks

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IoT data analytic algorithms on edge-cloud infrastructure:A review

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Digital Communications and Networks 2023年第6期9卷 1486-1515页

作者： Abel E.Edje M.S.Abd Latiff Weng Howe Chan School of Computing Faculty of EngineeringUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia Department of Computer Science Delta State UniversityAbrakaP.M.B 01 AbrakaDelta StateNigeria UTM Big Data Centre Ibnu Sina Institute of Scientific and Industrial ResearchUniversiti Teknologi Malaysia81310SkudaiJohorMalaysia

The adoption of Internet of Things(IoT)sensing devices is growing rapidly due to their ability to provide realtime ***,it is constrained by limited data storage and processing *** offloads its massive data stream to edge devices and the cloud for adequate storage and *** further leads to the challenges of data outliers,data redundancies,and cloud resource load balancing that would affect the execution and outcome of data *** paper presents a review of existing analytics algorithms deployed on IoT-enabled edge cloud infrastructure that resolved the challenges of data outliers,data redundancies,and cloud resource load *** review highlights the problems solved,the results,the weaknesses of the existing algorithms,and the physical and virtual cloud storage servers for resource load *** addition,it discusses the adoption of network protocols that govern the interaction between the three-layer architecture of IoT sensing devices enabled edge cloud and its prevailing challenges.A total of 72 algorithms covering the categories of classification,regression,clustering,deep learning,and optimization have been *** classification approach has been widely adopted to solve the problem of redundant data,while clustering and optimization approaches are more used for outlier detection and cloud resource allocation.

关键词： Internet of things Cloud platform Edge Analytic algorithms Processes Network communication protocols

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Improved Safety and 3D Scanning with Human-Robot Collaboration

Improved Safety and 3D Scanning with Human-Robot Collaborati...

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Proceedings of the Digital Image computing: Technqiues and Applications (DICTA)

作者： Madison Wright Karlym Nam Jinguang Tong Sundaram Muthu Lars Andersson Chuong Nguyen Imaging & Computer Vision - Data61 CSIRO Canberra Australia College of Eng. Computing & Cybernetics the Australian National University School of Electrical Eng. & Computer Science the University of Queensland

ISBN: (数字)9798350379037

ISBN: (纸本)9798350379044

In many technical working environments, humans need robots to perform, or assist with, repetitive tasks. Since robots remain a tool from our perspective, the question is how to safely use this tool to its full potential. We focus on two use cases: one is safety and the other is knowledge sharing. First, collaborative robots, or cobots, have been used more and more in factory work cells due to their ability to stop and prevent serious injuries when they collide to human operators. It would be even better if the collision is avoided in the first place with robotic vision. Second, robots could carry out tasks but may lack the knowledge or skill to deal with difficult situations, for example: completing a full 3D scanning of an object. In this paper, we present two approaches to improve safety and 3D scanning in a research lab environment. The setup is a work cell where a human operator works with a robot arm which is equipped with an RGBD camera and a gripper to manipulate and scan objects of interest. A second RGBD camera looks at the work cell for human gestures and their body movement. Robotic Operating System (ROS) version 2 is employed for hardware-software integration. One of our approaches allows the robot to track and follow objects of interest as they are moved around on a table, and stop when it detects a human hand in the way. Our other approach allows the robot to recognise human gestures to capture RGBD data from multiple views for 3D scanning and flip the object for scanning the hidden side. The captured data is then combined to have a more complete 3D information of the object. We demonstrate the effectiveness of our robot performing these tasks and the improvement in work safety and the quality of 3D object reconstruction.

关键词： Three-dimensional displays Tracking Robot vision systems Collaborative robots Cameras Production facilities Safety Collision avoidance Robots Image reconstruction

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