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ToPoFM: Topology-Guided Pathology Foundation Model for High-Resolution Pathology Image Synthesis with Cellular-Level Control

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IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Li, Jingxiong Zhu, Chenglu Zheng, Sunyi Chen, Pingyi Sun, Yuxuan Li, Honglin Yang, Lin Zhejiang University College of Computer Science and Technology Hangzhou310027 China Westlake University School of Engineering Hangzhou310024 China Tianjin Medical University Cancer Institute and Hospital National Clinical Research Center for Cancer Tianjin's Clinical Research Center for Cancer State Key Laboratory of Druggability Evaluation and Systematic Translational Medicine Tianjin Key Laboratory of Digestive Cancer Key Laboratory of Cancer Prevention and Therapy Department of Radiology Tianjin China Westlake University Artificial Intelligence and Biomedical Image Analysis Lab School of Engineering Hangzhou310024 China Westlake Institute for Advanced Study Institute of Advanced Technology Hangzhou310024 China Muyuan Laboratory Center for Interdisciplinary Research and Innovation Henan Zhengzhou450016 China

Synthetic data generation emerges as a strategy to mitigate data scarcity in digital pathology, where complicated tissue and cellular features are correlated with cancer diagnosis. The synthesis of such visuals, however, suffers from limited inter class diversity and scarcity of cellular annotations. Current methodologies struggle with capturing the broad spectrum of pathology features, causing unpredictable objects and defected fidelity. Moreover, discrepancies in image resolution across developmental and operational phases can amplify the distribution shifts, undermining the precision of diagnosis. To address these challenges, we introduce TOpology guided PathOlogy Foundation Model (ToPoFM), a visual foundation model designed for the synthesis of high-resolution pathology images with cellular-level control. Our approach integrates a topology-informed cell arrangement generator to steer large language models for crafting synthetic cell arrangements. We correlate cell arrangement guidance with diffusion model for pathology content generation, then further implement a random sliding inference strategy, merging discrete low-resolution samplings into single high-resolution representation. Our model requires only small patches for training. The efficacy of ToPoFM is demonstrated through extensive experiments, complemented by expert validations, showing high fidelity on data synthesis. Additionally, we underscore the utility of our generated imagery as an augmentation tool, enhancing the performance of downstream tasks, including cancer subtype classification and segmentation. © 2025 IEEE.

关键词： Image segmentation

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In-silico study of lanthanide-based nanoparticles for dual-modal photoacoustic and MRI theranostics

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Scientific Reports 2025年第1期15卷 1-14页

作者： Farid Alidoust Aghdam Ali Rostami Photonics and Nanocrystals Research Lab (PNRL) Faculty of Electrical and Computer Engineering University of Tabriz Tabriz Iran SP-EPT Lab. ASEPE Company Industrial Park of Advanced Technologies Tabriz Iran

This study presents a comprehensive theoretical assessment of lanthanide-based core-shell nanoparticles for theranostic applications in the near-infrared region. The complex refractive indices of fifteen lanthanide elements were calculated using density functional theory and the hybrid HSE06 method, which provided optical data in the spectral range of 100–2500 nm. Mie’s theoretical calculations for various biocompatible coatings (SiO2, PEG, TiO2) indicated that TiO2coatings exhibit better absorption efficiency in the NIR-I window (750–900 nm) (Qabsup to 9.4\(\times\)104). The optimal core size for TiO2-coated nanoparticles was determined to be between 90 and 110 nm, and the core-shell ratio ranged from 0.54 to 0.63, which provides the highest adsorption efficiency. Modeling biological heat transfer using the Pennes model with the finite element method revealed that the thermal reactions of tissues vary significantly for each tissue type. The thermal threshold time followed a power relation with . The fastest thermal response was observed in breast tissue (threshold time: 2.40–9.60 seconds), while the slowest response was noted in liver tissue (threshold time: 19.20–4.80 seconds). These findings offer key parameters for optimizing lanthanide-based therapeutic imaging platforms and provide a theoretical framework for predicting their performance in biological environments.

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Hippocampal Segmentation in Brain MRI Images Using Machine Learning Methods: A Survey

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Chinese Journal of Electronics 2021年第5期30卷 793-814页

作者： PAN Yi LIU Jin TIAN Xu LAN Wei GUO Rui Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Hunan Provincial Key Lab on Bioinformatics School of Computer Science and EngineeringCentral South University School of Computer Electronics and Information Guangxi University

The hippocampus is closely related to many brain diseases, such as Alzheimer's disease. Accurate measurement of the hippocampus is helpful for clinicians in identifying lesions and then diagnosing and treating the related brain diseases. Therefore, accurate segmentation of the hippocampus is of vital significance for the indepth study of many brain diseases. However, the accurate measurement of the hippocampus depends on its accurate segmentation, and hippocampal segmentation has always been a challenging problem due to the small size,irregular shape, and fuzzy boundaries with surrounding tissues of the hippocampus. With the development of machine learning, many innovative methods have been proposed to segment the hippocampus. The purpose of this survey is to provide a comprehensive overview of hippocampal segmentation in brain MRI images using machine learning methods. First, a brief introduction to hippocampal segmentation in brain MRI images is given. Then, common evaluation metrics of hippocampal segmentation are introduced. Next, brain hippocampal segmentation methods based on traditional machine learning and deep learning are described. Subsequently,some common open datasets and toolkits applied to brain hippocampal segmentation are presented. Finally,objective conclusions regarding hippocampal segmentation in brain MRI images using machine learning methods are drawn, and future developments and trends are identified for brain hippocampal segmentation.

关键词： biomedical MRI brain image segmentation machine learning methods brain hippocampal segmentation methods medical image processing neurophysiology related brain diseases diseases learning (artificial intelligence) hippocampus brain MRI images

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Sequence-Aware Online Container Scheduling with Reinforcement Learning in Parked Vehicle Edge Computing

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IEEE Transactions on Vehicular Technology 2025年

作者： Wu, Jianqiu Guo, Jianxiong Tang, Zhiqing Luo, Chuanwen Wang, Tian Jia, Weijia Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Department of Computer Science Zhuhai519087 China Beijing Normal University Advanced Institute of Natural Sciences Zhuhai519087 China Beijing Normal-Hong Kong Baptist University Guangdong Key Lab of AI and Multi-Modal Data Processing Zhuhai519087 China Beijing Forestry University School of Information Science and Technology Beijing100083 China Engineering Research Center for Forestry-Oriented Intelligent Information Processing of National Forestry and Grassland Administration Beijing100083 China

Intelligent vehicles, often parked for long periods, are ideally suited to serve as computational nodes to expand the Mobile Edge Computing (MEC) infrastructure, with containerization significantly enhancing the system's load balancing, self-healing, resource isolation, and security. However, fluctuations in task demand and frequent container image downloads during peak hours create high loads on containerized nodes, as multiple mobile devices offload tasks simultaneously, leading to significant processing delays. Many existing studies make the simplified assumption of predefined patterns of task arrivals, which overlooks this issue and makes suboptimal decisions. In this paper, we consider a Parked Vehicles (PVs)-extended MEC scenario, where multiple devices request services on PVs functioning as edge servers, all controlled by a central base station. Task arrivals follow observed patterns based on long-term trends, such as peak and off-peak periods, resembling realistic arrival patterns rather than predefined ones. To optimize task offloading by identifying these patterns, we propose the Sequence-Aware Task Scheduling (SATS) algorithm, which is a policy gradient-based deep reinforcement learning approach that integrates Transformer and LSTM architectures to capture patterns in time-series task arrivals and relationships between nodes in a collaborative and containerized environment, thereby enhancing the efficiency of online task scheduling. The primary objective of SATS is to optimize the task offloading policy and minimize delay and energy consumption for all devices and PVs. Extensive numerical comparisons against baselines demonstrate the effectiveness and advantages of our algorithm. © 1967-2012 IEEE.

关键词： Mobile edge computing

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Adaptive Backdoor Attacks with Reasonable Constraints on Graph Neural Networks

arXiv

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arXiv 2025年

作者： Dong, Xuewen Li, Jiachen Li, Shujun You, Zhichao Qu, Qiang Kholodov, Yaroslav Shen, Yulong School of Computer Science and Technology Xidian University The Engineering Research Center of Blockchain Technology Application and Evaluation Ministry of Education China The Shaanxi Key Laboratory of Blockchain and Secure Computing Xi’an710071 China University of Kent CanterburyCT2 7NP United Kingdom Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences China Intelligent Transportation Systems Lab Innopolis University Innopolis Russia School of Computer Science and Technology Xidian University China The Shaanxi Key Laboratory of Network and System Security Xi’an710071 China

Recent studies show that graph neural networks (GNNs) are vulnerable to backdoor attacks. Existing backdoor attacks against GNNs use fixed-pattern triggers and lack reasonable trigger constraints, overlooking individual graph characteristics and rendering insufficient evasiveness. To tackle the above issues, we propose ABARC, the first Adaptive Backdoor Attack with Reasonable Constraints, applying to both graph-level and node-level tasks in GNNs. For graph-level tasks, we propose a subgraph backdoor attack independent of the graph’s topology. It dynamically selects trigger nodes for each target graph and modifies node features with constraints based on graph similarity, feature range, and feature type. For node-level tasks, our attack begins with an analysis of node features, followed by selecting and modifying trigger features, which are then constrained by node similarity, feature range, and feature type. Furthermore, an adaptive edge-pruning mechanism is designed to reduce the impact of neighbors on target nodes, ensuring a high attack success rate (ASR). Experimental results show that even with reasonable constraints for attack evasiveness, our attack achieves a high ASR while incurring a marginal clean accuracy drop (CAD). When combined with the state-of-the-art defense randomized smoothing (RS) method, our attack maintains an ASR over 94%, surpassing existing attacks by more than 7%. Copyright © 2025, The Authors. All rights reserved.

关键词： Graph neural networks

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Physicochemical Characterization Core Satellite Self-Assembled Magneto Hybrid Nanostructure Based on IONP/GNP/antiHER-2

SSRN

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SSRN 2025年

作者： Tavakolikia, Fatemeh Mirzapoor, Aboulfazl Abdolahad, Mohammad Department of Nanobiotechnology Faculty of Biological Sciences Tarbiat Modares University P.O. Box 14115-175 Tehran Iran Advanced and smart Nanobiosystems lab Faculty of Biological Sciences Tarbiat Modares University P.O. Box 14115-175 Tehran Iran Nano Electronic Center of Excellence Nano-bioelectronic Devices Lab. Cancer Electronics Research Group School of Electrical and Computer Eng. College of Engineering University of Tehran P.O. Box: 14395-515 Tehran Iran

With the growth of breast cancer statistics in women, studying and finding an effective treatment for this cancer has pulled into the consideration of researchers. Recently, drug delivery systems noticed in the diagnosis and treatment of cancer. The premise of these systems is the presence of a Nano carrier on which the drugs are set. One of the common Nano carriers is magnetic iron oxide nanoparticles, which is a suitable option as a Nano carrier due to its biocompatibility and easy disposal. Among the common antibodies in breast cancer cells, we can highlight the antiHER-2 to form smart drug delivery systems. Tetracycline is a group of antibiotics that proven to have anti-cancer properties, so this antibiotic can cause cancer cell apoptosis. In general, this research revolves around the design and fabrication of tetracycline of core satellite self-assembled magneto hybrid nanostructure based on magnetic Nano particles IONP/GNP/ targeted with antiHER-2 for targeted Breast Cancer treatment. The fabricated nanostructures showed good performance in drug delivery and reduction of cancer. © 2025, The Authors. All rights reserved.

关键词： Nanoclay

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Design and Fabrication of Core Satellite Self-Assembled Magneto Hybrid Nanostructure Based on Ionp/Gnp/Antiher-2 for Targeted Breast Cancer Treatment

SSRN

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SSRN 2025年

关键词： Nanoclay

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A probabilistic framework of preference discovery from folksonomy corpus

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Frontiers of computer Science 2017年第6期11卷 1075-1084页

作者： Xiaohui GUO Chunming HU Richong ZHANG Jinpeng HUAI State Key Lab of Software Development Environment Beihang University Beijing 100191 China Institute of Advanced Computing Technology School of Computer Science and Engineering Beihang University Beijing 100191 China

The increasing availability of folksonomy data makes them vital for user profiling approaches to precisely detect user preferences and better understand user interests, so as to render some personalized recommendation or re- trieval results. This paper presents a rigorous probabilis- tic framework to discover user preference from folkson- omy data. Furthermore, we incorporate three models into the framework with the corresponding inference methods, expectation-maximization or Gibbs sampling algorithms. The user preference is expressed through topical conditional distributions. Moreover, to demonstrate the versatility of our framework, a recommendation method is introduced to show the possible usage of our framework and evaluate the applica- bility of the engaged models. The experimental results show that, with the help of the proposed framework, the user pref- erence can be effectively discovered.

关键词： preference discovery tagging folksonomy so-cial annotation

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Optimal local register allocation for a multiple-issue machine 94

Optimal local register allocation for a multiple-issue machi...

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Proceedings of the 1994 International Conference on Supercomputing

作者： Meleis, W.M. Davidson, E.S. Advanced Computer Architecture Lab Department of Electrical Engineering and Computer Science University of Michigan Ann Arbor MI

ISBN: (纸本)9780897916653

This paper presents an algorithm that allocates registers optimally for straight-line code running on a generic multi-issue computer. On such a machine, an optimal register allocation is one that minimizes the number of issue slots that the code requires. Optimal spill selection and load/store placement are used to minimize the number of additional issue slots needed, given a schedule for the non-memory reference instructions and a fixed number of available physical registers. The generic multi-issue machine model closely models the operation of vector and VLIW processors, and could be extended to model super-scalar processors. The algorithm uses dynamic programming to search the state space of feasible register allocations; implicit and explicit state pruning are used to make the problem tractable without sacrificing optimality. The optimal allocation produced by the algorithm for a substantial example is presented.

关键词： Dynamic programming

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Interactive media server with media synchronized RAID storage system

Interactive media server with media synchronized RAID storag...

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15th International Workshop on Network and Operating Systems Support for Digital Audio and Video, NOSSDAV 2005

作者： Lim, Seung-Ho Jeong, Yo-Won Park, Kyu-Ho Computer Engineering Research Lab. EECS Korea Advanced Institute of Science and Technology

We propose an efficient placement algorithm and per-disk prefetching method to effectively support interactive operations in the media server. Our placement policy is incorporated with an encoder having a special bitcount control scheme that repeatedly tunes quantization parameters to adjust the bitcounts of video frames. This encoder can generate coded frames whose sizes are synchronized with the RAID stripe size, so that when various fast-forward levels are accessed we can reduce the seek and rotational latency and enhance the disk throughput of each disk in the RAID system. In the experimental results, the proposed placement policy and bitrate control scheme can significantly improve the average service time, which can enlarge the capacity of the interactive media server. Copyright 2005 ACM.

关键词： Interactive computer systems

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