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Enhancing Spatiotemporal Disease Progression Models via Latent Diffusion and Prior Knowledge

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arXiv 2024年

作者： Puglisi, Lemuel Alexander, Daniel C. Ravì, Daniele Dept. of Math and Computer Science University of Catania Italy Centre for Medical Image Computing University College London United Kingdom School of Physics Engineering and CS University of Hertfordshire United Kingdom

In this work, we introduce Brain Latent Progression (BrLP), a novel spatiotemporal disease progression model based on latent diffusion. BrLP is designed to predict the evolution of diseases at the individual level on 3D brain MRIs. Existing deep generative models developed for this task are primarily data-driven and face challenges in learning disease progressions. BrLP addresses these challenges by incorporating prior knowledge from disease models to enhance the accuracy of predictions. To implement this, we propose to integrate an auxiliary model that infers volumetric changes in various brain regions. Additionally, we introduce Latent Average Stabilization (LAS), a novel technique to improve spatiotemporal consistency of the predicted progression. BrLP is trained and evaluated on a large dataset comprising 11,730 T1-weighted brain MRIs from 2,805 subjects, collected from three publicly available, longitudinal Alzheimer’s Disease (AD) studies. In our experiments, we compare the MRI scans generated by BrLP with the actual follow-up MRIs available from the subjects, in both cross-sectional and longitudinal settings. BrLP demonstrates significant improvements over existing methods, with an increase of 22% in volumetric accuracy across AD-related brain regions and 43% in image similarity to the ground-truth scans. The ability of BrLP to generate conditioned 3D scans at the subject level, along with the novelty of integrating prior knowledge to enhance accuracy, represents a significant advancement in disease progression modeling, opening new avenues for precision medicine. The code of BrLP is available at the following link: https://***/LemuelPuglisi/BrLP. © 2024, CC BY.

关键词： Brain

P3GNN: A Privacy-Preserving Provenance Graph-Based Model for APT Detection in Software Defined Networking

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arXiv 2024年

作者： Nazari, Hedyeh Yazdinejad, Abbas Dehghantanha, Ali Zarrinkalam, Fattane Srivastava, Gautam Cyber Science Lab Canada Cyber Foundry University of Guelph Canada Dept. of Engineering University of Guelph Canada Dept. of Math and Computer Science Brandon University Manitoba Canada Research Centre for Interneural Computing China Medical University Taichung Taiwan

Software Defined Networking (SDN) has brought significant advancements in network management and programmability. However, this evolution has also heightened vulnerability to Advanced Persistent Threats (APTs), sophisticated and stealthy cyberattacks that traditional detection methods often fail to counter, especially in the face of zero-day exploits. A prevalent issue is the inadequacy of existing strategies to detect novel threats while addressing data privacy concerns in collaborative learning scenarios. This paper presents P3GNN (privacy-preserving provenance graph-based graph neural network (GNN) model), a novel model that synergizes Federated Learning (FL) with Graph Convolutional Networks (GCN) for effective APT detection in SDN environments. P3GNN utilizes unsupervised learning to analyze operational patterns within provenance graphs, identifying deviations indicative of security breaches. Its core feature is the integration of FL with homomorphic encryption, which fortifies data confidentiality and gradient integrity during collaborative learning. This approach addresses the critical challenge of data privacy in shared learning contexts. Key innovations of P3GNN include its ability to detect anomalies at the node level within provenance graphs, offering a detailed view of attack trajectories and enhancing security analysis. Furthermore, the model's unsupervised learning capability enables it to identify zero-day attacks by learning standard operational patterns. Empirical evaluation using the DARPA TCE3 dataset demonstrates P3GNN's exceptional performance, achieving an accuracy of 0.93 and a low false positive rate of 0.06. © 2024, CC BY.

关键词： Federated learning

Optimized Predictive Storage Resource Allocation Through Neural Forecasting for Cloud Environments

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Optimized Predictive Storage Resource Allocation Through Neu...

2024 International Conference on Innovative computing, Intelligent Communication and Smart Electrical Systems, ICSES 2024

作者： Sridevi Ponmalar, P. Adithya Pothan Raj, V. Jebaseelan, S. D. Sundarsingh Phade, Gayatri Srithar, V. Harikrishnan, R. School of computing Srm Institute of Science and Technology Department of Computational Intelligence Kattankulathur Campus Chennai India Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Dept. of Cse Chennai India Sathyabama Institute of Science and Technology Dept of Eee Chennai119 India Sandip Institute of Technology & Research Centre Nashik India PeriyarManiammai Institute of Science & Technology Department of Computer Applications Thanjavur India Symbiosis Institute of Technology Symbiosis International Deemed University Pune Campus Pune412115 India

ISBN: (纸本)9798331543617

The demand for efficient and scalable management of storage resources is growing enormously with the rapid growth in data-intensive applications within the cloud environment. Optimized PSRANF is the state-of-the-art approach towards dynamic storage resource allocation, based on the forecast of future demands in storage. PSRANF utilizes state-of-the-art neural forecasting with Long Short-Term Memory (LSTM) networks and Transformer designs to analyze the pattern of historical storage usage for highly accurate future need predictions. By proactively adjusting storage capacity to these predictions, PSRANF strikes an optimal balance between performance and cost by reserving just-in-time resources. It ensures that there is no risk of over-provisioning, which would be too costly. In the same breath, the risk of under-provisioning, which leads to performance bottlenecks, is avoided. What is more, the system will apply prioritization, placing the data of higher priority or frequently accessed data into faster storage tiers of a higher class of performance and less important classes of data into cost-effective storage. The adaptive feedback loop continuously refines the forecasting model, which, over time, improves accuracy and allows the system to handle changes in workload patterns. PSRANF combines neural forecasting with resource allocation in such a way that is cost-effective;thus, it is very well placed for cloud environments where the demand for storage is dynamic in nature and requires intelligent data-driven approaches. The AWS implementation of PSRANF has turned out to be quite a helpful tool in optimizing the storage resource allocation process. While it is able to predict 98.5% of the storage needs correctly by achieving a cost saving of up to 18.7%, managing data from 97.8% to 99.2%, and resources from 95.4%, it's robust for improvements in cloud storage management and operational efficiency. © 2024 IEEE.

关键词： Storage efficiency

Brain Latent Progression: Individual-based Spatiotemporal Disease Progression on 3D Brain MRIs via Latent Diffusion

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arXiv 2025年

作者： Puglisi, Lemuel Alexander, Daniel C. Ravì, Daniele Dept. of Math and Computer Science University of Catania Viale Andrea Doria 6 Catania Italy Centre for Medical Image Computing University College London 90 High Holborn London United Kingdom MIFT Department University of Messina Viale Ferdinando Stagno d’Alcontres 31 Messina Italy

The growing availability of longitudinal Magnetic Resonance Imaging (MRI) datasets has facilitated Artificial Intelligence (AI)-driven modeling of disease progression, making it possible to predict future medical scans for individual patients. However, despite significant advancements in AI, current methods continue to face challenges including achieving patient-specific individualization, ensuring spatiotemporal consistency, efficiently utilizing longitudinal data, and managing the substantial memory demands of 3D scans. To address these challenges, we propose Brain Latent Progression (BrLP), a novel spatiotemporal model designed to predict individual-level disease progression in 3D brain MRIs. The key contributions in BrLP are fourfold: (i) it operates in a small latent space, mitigating the computational challenges posed by high-dimensional imaging data;(ii) it explicitly integrates subject metadata to enhance the individualization of predictions;(iii) it incorporates prior knowledge of disease dynamics through an auxiliary model, facilitating the integration of longitudinal data;and (iv) it introduces the Latent Average Stabilization (LAS) algorithm, which (a) enforces spatiotemporal consistency in the predicted progression at inference time and (b) allows us to derive a measure of the uncertainty for the prediction. We train and evaluate BrLP on 11,730 T1-weighted (T1w) brain MRIs from 2,805 subjects and validate its generalizability on an external test set comprising 2,257 MRIs from 962 subjects. Our experiments compare BrLP-generated MRI scans with real follow-up MRIs, demonstrating state-of-the-art accuracy compared to existing methods. The code is publicly available at: https://***/LemuelPuglisi/BrLP. © 2025, CC BY.

关键词： Magnetic resonance imaging

A Federated Learning Approach for Multi-stage Threat Analysis in Advanced Persistent Threat Campaigns

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arXiv 2024年

作者： Nelles, Florian Yazdinejad, Abbas Dehghantanha, Ali Parizi, Reza M. Srivastava, Gautam The Faculty of Computer Science Deggendorf Institute of Technology Dieter-Görlitz-Platz 1 Deggendorf94469 Germany The Cyber Science Lab Canada Cyber Foundry University of Guelph ON Canada The Decentralized Science Lab Kennesaw State University GA United States The Department of Math and Computer Science Brandon University Brandon Canada The Research Centre for Interneural Computing China Medical University Taichung40402 Taiwan The Dept. of Computer Science and Math Lebanese American University Beirut1102 Lebanon

Multi-stage threats like advanced persistent threats (APT) pose severe risks by stealing data and destroying infrastructure, with detection being challenging. APTs use novel attack vectors and evade signature-based detection by obfuscating their network presence, often going unnoticed due to their novelty. Although machine learning models offer high accuracy, they still struggle to identify true APT behavior, overwhelming analysts with excessive data. Effective detection requires training on multiple datasets from various clients, which introduces privacy issues under regulations like GDPR. To address these challenges, this paper proposes a novel 3-phase unsupervised federated learning (FL) framework to detect APTs. It identifies unique log event types, extracts suspicious patterns from related log events, and orders them by complexity and frequency. The framework ensures privacy through a federated approach and enhances security using Paillier's partial homomorphic encryption. Tested on the SoTM 34 dataset, our framework compares favorably against traditional methods, demonstrating efficient pattern extraction and analysis from log files, reducing analyst workload, and maintaining stringent data privacy. This approach addresses significant gaps in current methodologies, offering a robust solution to APT detection in compliance with privacy laws. © 2024, CC BY.

关键词： Data privacy

Optimized Predictive Storage Resource Allocation Through Neural Forecasting for Cloud Environments

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Optimized Predictive Storage Resource Allocation Through Neu...

Innovative computing, Intelligent Communication and Smart Electrical Systems (ICSES), International Conference on

作者： Sridevi Ponmalar. P Adithya Pothan Raj V S D Sundarsingh Jebaseelan Gayatri Phade V. Srithar Harikrishnan. R Department of Computational Intelligence School of computing Kattankulathur Campus SRM Institute of Science and Technology Chennai Dept. of CSE Saveetha School of Engineering Saveetha Institute of Medical and Technical Sciences Chennai India Dept of EEE Sathyabama Institute of Science and Technology Chennai 119 Sandip Institute of Technology & Research Centre Nashik Department of Computer Applications PeriyarManiammai Institute of Science & Technology Thanjavur Symbiosis Institute of Technology Pune Campus Symbiosis International Deemed University Pune

ISBN: (数字)9798331543624

ISBN: (纸本)9798331543631

The demand for efficient and scalable management of storage resources is growing enormously with the rapid growth in data-intensive applications within the cloud environment. Optimized PSRANF is the state-of-the-art approach towards dynamic storage resource allocation, based on the forecast of future demands in storage. PSRANF utilizes state-of-the-art neural forecasting with Long Short-Term Memory (LSTM) networks and Transformer designs to analyze the pattern of historical storage usage for highly accurate future need predictions. By proactively adjusting storage capacity to these predictions, PSRANF strikes an optimal balance between performance and cost by reserving just-in-time resources. It ensures that there is no risk of over-provisioning, which would be too costly. In the same breath, the risk of under-provisioning, which leads to performance bottlenecks, is avoided. What is more, the system will apply prioritization, placing the data of higher priority or frequently accessed data into faster storage tiers of a higher class of performance and less important classes of data into cost-effective storage. The adaptive feedback loop continuously refines the forecasting model, which, over time, improves accuracy and allows the system to handle changes in workload patterns. PSRANF combines neural forecasting with resource allocation in such a way that is cost-effective; thus, it is very well placed for cloud environments where the demand for storage is dynamic in nature and requires intelligent data-driven approaches. The AWS implementation of PSRANF has turned out to be quite a helpful tool in optimizing the storage resource allocation process. While it is able to predict 98.5% of the storage needs correctly by achieving a cost saving of up to 18.7 % , managing data from 97.8% to 99.2%, and resources from 95.4%, it's robust for improvements in cloud storage management and operational efficiency.

关键词： Cloud computing Costs Accuracy Storage management Predictive models Dynamic scheduling Resource management Forecasting Optimization Long short term memory

GPT (Generative Pre-trained Transformer) - A Comprehensive Review on Enabling Technologies, Potential Applications, Emerging Challenges, and Future Directions

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arXiv 2023年

作者： Yenduri, Gokul Ramalingam, M. Selvi, Chemmalar G. Supriya, Y. Srivastava, Gautam Maddikunta, Praveen Kumar Reddy Raj, Deepti G. Jhaveri, Rutvij H. Prabadevi, B. Wang, Weizheng Vasilakos, Athanasios V. Gadekallu, Thippa Reddy The School of Information Technology and Engineering Vellore Institute of Technology Tamil Nadu Vellore632014 India The Dept. of Math and Computer Science Brandon University Canada The Research Centre for Interneural Computing China Medical University Taichung Taiwan Dept. of Computer Science and Math Lebanese American University Beirut Lebanon The Department of Computer Science and Engineering School of Technology Pandit Deendayal Energy University India The Department of Computer Science City University of Hong Kong Hong Kong Grimstad Norway Lovely Professional University Phagwara India Department of Electrical and Computer Engineering Lebanese American University Byblos Lebanon Jiaxing University Jiaxing314001 China Zhongda Group 314312 China

The Generative Pre-trained Transformer (GPT) represent a notable breakthrough in the domain of natural language processing, which is propelling us toward the development of machines that can understand and communicate using language in a manner that closely resembles that of humans. GPT is based on the transformer architecture, a deep neural network designed for natural language processing tasks. Due to their impressive performance on natural language processing tasks and ability to effectively converse, GPT have gained significant popularity among researchers and industrial communities, making them one of the most widely used and effective models in natural language processing and related fields, which motivated to conduct this review. This review provides a detailed overview of the GPT, including its architecture, working process, training procedures, enabling technologies, and its impact on various applications. In this review, we also explored the potential challenges and limitations of a GPT. Furthermore, we discuss potential solutions and future directions. Overall, this paper aims to provide a comprehensive understanding of GPT, enabling technologies, their impact on various applications, emerging challenges, and potential solutions. © 2023, CC BY.

关键词： Deep neural networks

Corrigendum to "SANDI: A compartment-based model for non-invasive apparent soma and neurite imaging by diffusion MRI" [Neuroimage 215 (2020), 116835]

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Neuroimage 2021年 226卷 117612页

作者： Marco Palombo Andrada Ianus Michele Guerreri Daniel Nunes Daniel C Alexander Noam Shemesh Hui Zhang Centre for Medical Image Computing and Dept of Computer Science University College London London UK. Electronic address: marco.palombo@ucl.ac.uk. Centre for Medical Image Computing and Dept of Computer Science University College London London UK Champalimaud Research Champalimaud Centre for the Unknown Lisbon Portugal. Centre for Medical Image Computing and Dept of Computer Science University College London London UK.

Understanding metric-related pitfalls in image analysis validation

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arXiv 2023年

作者： Reinke, Annika Tizabi, Minu D. Baumgartner, Michael Eisenmann, Matthias Heckmann-Nötzel, Doreen Kavur, A. Emre Rädsch, Tim Sudre, Carole H. Acion, Laura Antonelli, Michela Arbel, Tal Bakas, Spyridon Benis, Arriel Blaschko, Matthew B. Buettner, Florian Cardoso, M. Jorge Cheplygina, Veronika Chen, Jianxu Christodoulou, Evangelia Cimini, Beth A. Collins, Gary S. Farahani, Keyvan Ferrer, Luciana Galdran, Adrian van Ginneken, Bram Glocker, Ben Godau, Patrick Haase, Robert Hashimoto, Daniel A. Hoffman, Michael M. Huisman, Merel Isensee, Fabian Jannin, Pierre Kahn, Charles E. Kainmueller, Dagmar Kainz, Bernhard Karargyris, Alexandros Karthikesalingam, Alan Kenngott, Hannes Kleesiek, Jens Kofler, Florian Kooi, Thijs Kopp-Schneider, Annette Kozubek, Michal Kreshuk, Anna Kurc, Tahsin Landman, Bennett A. Litjens, Geert Madani, Amin Maier-Hein, Klaus Martel, Anne L. Mattson, Peter Meijering, Erik Menze, Bjoern Moons, Karel G.M. Müller, Henning Nichyporuk, Brennan Nickel, Felix Petersen, Jens Rafelski, Susanne M. Rajpoot, Nasir Reyes, Mauricio Riegler, Michael A. Rieke, Nicola Saez-Rodriguez, Julio Sánchez, Clara I. Shetty, Shravya Summers, Ronald M. Taha, Abdel A. Tiulpin, Aleksei Tsaftaris, Sotirios A. van Calster, Ben Varoquaux, Gaël Yaniv, Ziv R. Jäger, Paul F. Maier-Hein, Lena Faculty of Mathematics and Computer Science Heidelberg University Heidelberg Germany Heidelberg Division of Intelligent Medical Systems Germany NCT Heidelberg A Partnership Between DKFZ University Medical Center Heidelberg Germany Heidelberg Division of Medical Image Computing Germany Heidelberg Division of Intelligent Medical Systems Germany MRC Unit for Lifelong Health and Ageing UCL Centre for Medical Image Computing Department of Computer Science University College London London United Kingdom School of Biomedical Engineering and Imaging Science King’s College London London United Kingdom Instituto de Cálculo CONICET – Universidad de Buenos Aires Buenos Aires Argentina Centre for Medical Image Computing University College London London United Kingdom McGill University Montreal Canada Division of Computational Pathology Dept of Pathology & Laboratory Medicine Indiana University School of Medicine IU Health Information and Translational Sciences Building Indianapolis United States University of Pennsylvania Richards Medical Research Laboratories FL7 PhiladelphiaPA United States Department of Digital Medical Technologies Holon Institute of Technology Holon Israel European Federation for Medical Informatics Le Mont-sur-Lausanne Switzerland Center for Processing Speech and Images Department of Electrical Engineering KU Leuven Leuven Belgium partner site Frankfurt/Mainz a partnership between DKFZ and UCT Frankfurt Marburg Germany Heidelberg Germany Goethe University Frankfurt Department of Medicine Germany Goethe University Frankfurt Department of Informatics Germany and Frankfurt Cancer Insititute Germany Department of Computer Science IT University of Copenhagen Copenhagen Denmark Leibniz-Institut für Analytische Wissenschaften – ISAS – e.V. Dortmund Germany Imaging Platform Broad Institute of MIT and Harvard CambridgeMA United States Centre for Statistics in Medicine University of Oxford Oxford United Kingdom Center for Biomedical In

Validation metrics are key for tracking scientific progress and bridging the current chasm between artificial intelligence (AI) research and its translation into practice. However, increasing evidence shows that particularly in image analysis, metrics are often chosen inadequately. While taking into account the individual strengths, weaknesses, and limitations of validation metrics is a critical prerequisite to making educated choices, the relevant knowledge is currently scattered and poorly accessible to individual researchers. Based on a multi-stage Delphi process conducted by a multidisciplinary expert consortium as well as extensive community feedback, the present work provides the first reliable and comprehensive common point of access to information on pitfalls related to validation metrics in image analysis. While focused on biomedical image analysis, the addressed pitfalls generalize across application domains and are categorized according to a newly created, domain-agnostic taxonomy. The work serves to enhance global comprehension of a key topic in image analysis validation. © 2023, CC BY.

关键词： computer vision