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22nd IEEE International Symposium on Biomedical Imaging, ISBI 2025

作者： Nguyen, Manh Duong Pham, Nguyen Dang Huy Nguyen, Phi Le Do, Minh N. Institute for Ai Innovation and Socital Impact (AI4LIFE) Hanoi University of Science and Technology Hanoi Viet Nam University of Illinois at Urbana-Champaign Coordinated Science Laboratory United States Hanoi - Amsterdam High School for the Gifted Viet Nam University of Illinois at Urbana-Champaign Department of Electrical and Computer Engineering United States VinUniversity VinUni-Illinois Smart Health Center Viet Nam

ISBN: (纸本)9798331520526

Mitotic figure counting plays a critical role in glioma grading and prognostication, yet manual counting remains time-consuming and subject to variability. The Glioma-MDC 2025 Challenge, hosted at ISBI 2025, aims to address this challenge by advancing automated solutions. In response, we present SEMA, a semi-supervised learning framework that incorporates a cross-magnification attention mechanism. SEMA mimics the multi-magnification workflow used by pathologists, employing attention to model interactions between cells and neighboring regions, effectively replicating how pathologists analyze whole slide images. Additionally, SEMA leverages the abundance of unlabeled cells in raw tissue images from competition datasets through a semi-supervised learning approach, progressively enhancing its robustness and performance. Extensive experiments demonstrate that SEMA outperforms other baselines by up to 40% in FI score, with each component contributing significantly to its success. Notably, SEMA achieves a perfect F1 score, securing top performance on the public leaderboard. © 2025 IEEE.

关键词： foundation model Glioma mitosis multi-magnification semi-supervised

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Interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning for drug-related side effect prediction

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computers in Biology and Medicine 2025年 184卷 109321-109321页

作者： Xuan, Ping Wu, Shien Cui, Hui Li, Peiru Nakaguchi, Toshiya Zhang, Tiangang Department of Computer Science and Technology Shantou University Shantou China School of Cyberspace Security Hainan University Haikou China Department of Computer Science and Information Technology La Trobe University Australia Australian Centre for AI in Medical Innovation La Trobe University Australia School of Computer Science and Technology Heilongjiang University Harbin China Center for Frontier Medical Engineering Chiba University Japan

Identifying the potential side effects for the interested drugs can help reduce harm to patients caused by drugs in clinical use and decrease the risk of drug development failure. Multiple functionally similar drugs often have multiple similar side effects, resulting in the closed relationships among these nodes. However, most of previous methods did not completely encode the features from the biological perspective to mine the complex associations between the drugs and side effects. A prediction model based on interactive multi-hypergraph inferring and channel-enhanced and attribute-enhanced learning, ICAL, was proposed to fuse the global correlations reflected by multiple hypergraphs and to learn the attributes of a pair of drug and side effect nodes enhanced by the channels and attributes. First, we designed a hypergraph architecture where a hyperedge reflects the complex correlations between a single drug (side effect) and all the drugs and side effects, and the entire hypergraph composed of the hyperedges reveals the global correlations of all the drugs and side effects. Two hypergraphs were established based on two types of drug similarities, and each hypergraph implies its specific complex relationships among multiple drugs and side effects. Second, we proposed an interactive hypergraph neural network to enable the learning of global correlation features of drugs and side effects from the two hypergraphs. It propagated the node features across multiple hypergraphs and encoded the context relationships within these hypergraphs. Besides, the attentions at the channel level and at the attribute level were proposed to integrate the semantic correlations among multiple channels and to encode the long-distance dependence within the attributes of a pair of drug and side effect. The experimental results based on cross-validation showed that our new model outperformed seven advanced prediction methods in terms of AUC, AUPR, and recall rates for the top-ranked candidat

关键词： Prediction models

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Joint Self-Supervised Image-Volume Representation Learning with Intra-inter Contrastive Clustering 37

Joint Self-Supervised Image-Volume Representation Learning w...

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37th AAai Conference on Artificial Intelligence, AAai 2023

作者： Nguyen, Duy M.H. Nguyen, Hoang Mai, Truong T.N. Cao, Tri Nguyen, Binh T. Ho, Nhat Swoboda, Paul Albarqouni, Shadi Xie, Pengtao Sonntag, Daniel Department of Computer Science University of Stuttgart Germany AISIA Lab. University of Science - VNU HCM Viet Nam Department of Multimedia Engineering Dongguk University Korea Republic of Department of Statistics and Data Sciences University of Texas Austin United States Max Planck Institute for Informatics Germany Helmholtz AI Helmholtz Munich Germany Clinic for Diagnostic and Interventional Radiology University of Bonn Germany Department of Electrical and Computer Engineering University of California San Diego United States Department of Computer Science Oldenburg University Germany German Research Center for Artificial Intelligence Germany

ISBN: (纸本)9781577358800

Collecting large-scale medical datasets with fully annotated samples for training of deep networks is prohibitively expensive, especially for 3D volume data. Recent breakthroughs in self-supervised learning (SSL) offer the ability to overcome the lack of labeled training samples by learning feature representations from unlabeled data. However, most current SSL techniques in the medical field have been designed for either 2D images or 3D volumes. In practice, this restricts the capability to fully leverage unlabeled data from numerous sources, which may include both 2D and 3D data. Additionally, the use of these pre-trained networks is constrained to downstream tasks with compatible data dimensions. In this paper, we propose a novel framework for unsupervised joint learning on 2D and 3D data modalities. Given a set of 2D images or 2D slices extracted from 3D volumes, we construct an SSL task based on a 2D contrastive clustering problem for distinct classes. The 3D volumes are exploited by computing vectored embedding at each slice and then assembling a holistic feature through deformable self-attention mechanisms in Transformer, allowing incorporating long-range dependencies between slices inside 3D volumes. These holistic features are further utilized to define a novel 3D clustering agreement-based SSL task and masking embedding prediction inspired by pre-trained language models. Experiments on downstream tasks, such as 3D brain segmentation, lung nodule detection, 3D heart structures segmentation, and abnormal chest X-ray detection, demonstrate the effectiveness of our joint 2D and 3D SSL approach. We improve plain 2D Deep-ClusterV2 and SwAV by a significant margin and also surpass various modern 2D and 3D SSL approaches. Copyright © 2023, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

关键词： Embeddings

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Design Automation for Continuous-Flow Lab-on-a-Chip Systems: A One-Pass Paradigm

Design Automation for Continuous-Flow Lab-on-a-Chip Systems:...

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作者： Huang, Xing Pan, Youlin Chen, Zhen Guo, Wenzhong Wang, Lu Li, Qingshan Wille, Robert Ho, Tsung-Yi Schlichtmann, Ulf Technical University of Munich Electronic Design Automation Munich80333 Germany Fuzhou University College of Computer and Data Science Fuzhou350116 China Xidian University School of Computer Science and Technology Xi'an710071 China Technical University of Munich Chair for Design Automation Munich80333 Germany Software Competence Center Hagenberg GmbH Hagenberg4232 Austria The Chinese University of Hong Kong Department of Computer Science and Engineering Hong Kong

Owing to the high complexity of chip architecture and assay protocol, considerable effort has been directed toward the design automation of continuous-flow microfluidics over the past decade. Existing methods, however, perform the corresponding design tasks, including binding, scheduling, placement, and routing separately, leading to serious gaps between different steps and potentially even cause design failure. To overcome these drawbacks, in this article, we propose a one-pass design paradigm for continuous-flow microfluidic lab-on-a-chip systems, integrating all the design steps into an 'organic whole,' which has never been considered in prior work. With the proposed paradigm, all the design tasks can be synchronized seamlessly and performed in a combined manner, thereby eliminating the gaps between design steps. Consequently, optimized biochip architectures can be generated without any design adjustments and modifications. The experimental results demonstrate the effectiveness of the proposed automation flows. © 1982-2012 IEEE.

关键词： Microfluidics

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Framework for Fuzzy Temporal Association Rule Mining Based on Density Clustering Optimization

Framework for Fuzzy Temporal Association Rule Mining Based o...

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6th International Conference on Intelligent Computing and Signal Processing (ICSP)

作者： Yang Cao Pengzhi Ma School of Computer Science and Engineering Shenyang Jianzhu University(SJZU) Liaoning Province Big Data Management and Analysis Laboratory of Urban Construction Shenyang Branch of National Special Computer Engineering Technology Research Center Shenyang China

ISBN: (数字)9798350376548

ISBN: (纸本)9798350376555

This paper proposes a framework for fuzzy temporal association rule mining based on density clustering optimization, aimed at improving the fuzzification of continuous attributes and enhancing the efficiency of mining temporal association rules. The first part of the framework effectively handles continuous attribute data and reduces data redundancy by adopting a density peak clustering algorithm and designing an optimized membership function. The second part introduces a fuzzy list ranking structure combined with item lifespan to mine fuzzy temporal association rules. Experimental results demonstrate that the framework exhibits superior performance in mining fuzzy temporal association rules and outperforms advanced algorithms in handling continuous attributes.

关键词： Fuzzy sets Systematics Redundancy Memory management Signal processing algorithms Clustering algorithms Signal processing Association rule learning Optimization Sorting

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DOUBLY ROBUST PROXIMAL CAUSAL LEARNING FOR CONTINUOUS TREATMENTS

arXiv

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arXiv 2023年

作者： Wu, Yong Fu, Yanwei Wang, Shouyan Sun, Xinwei Institute of Science and Technology for Brain-Inspired Intelligence Fudan University China School of Data Science Fudan University China Zhangjiang Fudan International Innovation Center China MOE Frontiers Center for Brain Science Fudan University China Shanghai Engineering Research Center of AI & Robotics Fudan University China Engineering Research Center of AI & Robotics Ministry of Education Fudan University China

Proximal causal learning is a powerful framework for identifying the causal effect under the existence of unmeasured confounders. Within this framework, the doubly robust (DR) estimator was derived and has shown its effectiveness in estimation, especially when the model assumption is violated. However, the current form of the DR estimator is restricted to binary treatments, while the treatments can be continuous in many real-world applications. The primary obstacle to continuous treatments resides in the delta function present in the original DR estimator, making it infeasible in causal effect estimation and introducing a heavy computational burden in nuisance function estimation. To address these challenges, we propose a kernel-based DR estimator that can well handle continuous treatments for proximal causal learning. Equipped with its smoothness, we show that its oracle form is a consistent approximation of the influence function. Further, we propose a new approach to efficiently solve the nuisance functions. We then provide a comprehensive convergence analysis in terms of the mean square error. We demonstrate the utility of our estimator on synthetic datasets and real-world applications1 Copyright © 2023, The Authors. All rights reserved.

关键词： Mean square error

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Leader and Follower: Interactive Motion Generation under Trajectory Constraints

arXiv

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arXiv 2025年

作者： Wang, Runqi Ma, Caoyuan Zhao, Jian Xu, Hanrui Sun, Dongfang Chen, Haoyang Xiong, Lin Wang, Zheng Li, Xuelong National Engineering Research Center for Multimedia Software Institute of Artificial Intelligence China School of Computer Science Wuhan University China The Institute of AI China Telecom China Geely Auto Research China

With the rapid advancement of game and film production, generating interactive motion from texts has garnered significant attention due to its potential to revolutionize content creation processes. In many practical applications, there is a need to impose strict constraints on the motion range or trajectory of virtual characters. However, existing methods that rely solely on textual input face substantial challenges in accurately capturing the user’s intent—particularly in specifying the desired trajectory. As a result, the generated motions often lack plausibility and accuracy. Moreover, existing trajectory-based methods for customized motion generation rely on retraining for single-actor scenarios, which limits flexibility and adaptability to different datasets, as well as interactivity in two-actor motions. To generate interactive motion following specified trajectories, this paper decouples complex motion into a Leader-Follower dynamic, inspired by role allocation in partner dancing. Based on this framework, this paper explores the motion range refinement process in interactive motion generation and proposes a training-free approach, integrating a Pace Controller and a Kinematic Synchronization Adapter. The framework enhances the ability of existing models to generate motion that adheres to trajectory by control- Copyright © 2025, The Authors. All rights reserved.

关键词： Trajectories

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SWAMamba: A Sliding Window Attention Mamba Framework for Predicting Translation Elongation Rates 39

SWAMamba: A Sliding Window Attention Mamba Framework for Pre...

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39th Annual AAai Conference on Artificial Intelligence, AAai 2025

作者： Zeng, Xi Ni, Fei Jiao, Shaoqing Lu, Dazhi Hao, Jianye Peng, Jiajie AI for Science Interdisciplinary Research Center School of Computer Science Northwestern Polytechnical University China College of Intelligence and Computing Tianjin University China School of Computer Science Research and Development Institute Northwestern Polytechnical University Shenzhen China Key Laboratory of Big Data Storage and Management Northwestern Polytechnical University Ministry of Industry and Information Technology China

ISBN: (纸本)157735897X

Translation elongation is essential for cellular proteostasis and is implicated in cancer and neurodegeneration. Accurately predicting the rate of ribosome elongation in each codon (also called ribosomal A site) on mRNA is important for understanding and modulating protein synthesis. However, predicting elongation rates is challenging due to the trade-off between capturing distal codon interactions and focusing on proximal codon effects at the A site. Approaches capturing distal codon interactions in the coding sequences (CDS) of mRNA fail to effectively differentiate critical regions (codons near the A site) due to insufficient effective mechanisms for focusing on these regions. Conversely, due to the limitations of models when handling long mRNA sequences, some methods simplify inputs by conditioning solely on proximal codons surrounding the A site, leading to the loss of important information from distal codons. To address this issue, we leverage Mamba’s success in capturing long-range dependencies to enable the consideration of distant codons’ impact on the A site. Additionally, we introduce a sliding window attention mechanism to emphasize the proximal codons around the A site during ribosome elongation. Building on these advancements, we present Sliding Window Attention Mamba (SWAMamba), a novel framework that simultaneously leverages both proximal and distal codon effects on the A site. We conduct comprehensive evaluations on ribosome data across four species and find that SWAMamba significantly outperformed current state-of-the-art methods in predicting translation elongation rates. © 2025, Association for the Advancement of Artificial Intelligence (***). All rights reserved.

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An efficient multi-attribute combinatorial auction for 4PL transportation service procurement

An efficient multi-attribute combinatorial auction for 4PL t...

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第35届中国控制与决策会议

作者： Na Yuan Haiming Liang Min Huang Yanfeng Zhang College of Information Science and Engineering Northeastern University Center for Network Big Data and Decision-Making Business School Sichuan University College of Computer Science and Engineering Northeastern University

In this paper,we study the mechanism design problem of combinatorial reverse auction for 4 PL transportation service procurement(TSP) considering price and multiple nonprice *** order to achieve efficient winner determination and transportation request allocation,we develop a 0-1 programing model to minimize social cost,where social cost includes real transportation cost of 3 PLs and the costs incurred by non-price attributes.A multi-attribute VCG(MA-VCG) reverse auction mechanism is proposed,which satisfies efficiency,incentive compatibility,individual rationality,and budget ***,a numerical example is designed to demonstrate the effectiveness and applicability of the proposed ***,sensitivity analysis is implemented to illustrate the influences of attribute preference,cost coefficient and other parameters on auction results.

关键词： Transportation service procurement Mechanism design Combinatorial reverse auction Multi-attribute auction

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Energy-efficient and Privacy-preserving Edge Intelligence for 6G-Empowered Intelligent Transportation Systems

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IEEE Network 2025年

作者： Zhu, Chenlu Fan, Xiaoxuan Deng, Xianjun Xiao, Ziheng Guo, Tingting Liu, Shenghao Sun, Jiaqi Park, Jong Hyuk Hubei Key Laboratory of Distributed System Security Hubei Engineering Research Center on Big Data Security School of Cyber Science and Engineering Huazhong University of Science and Technology Wuhan430074 China Wuhan Kedao Geographic Information Engineering Co. Wuhan China Department of Computer Science and Engineering Seoul National University of Science and Technology Seoul Korea Republic of

6G-empowered Intelligent Transportation Systems (ITS) generate large amounts of data through millions of devices and sensors at the terminal and network edge. Edge intelligence advances the frontier of data-driven artificial intelligence (ai) to the network edge, which enables ai applications to better utilize edge data. Broader connectivity, higher device density, and stronger data exchange present challenges for reliable device communication and privacy data protection in 6G-empowered ITS. To address these challenges, this paper proposes an energy-efficient and privacy-preserving edge intelligence framework named EP-EI. EP-EI deploys a deep reinforcement learning-based cluster head selection model at the edge to ensure energy-efficient communication for terminal devices. Meanwhile, EP-EI designs a privacy-aware hierarchical computing framework for terminal-edge-cloud to prevent privacy leakage. Furthermore, the application scenarios, challenges, and future directions of EP-EI are discussed. Finally, simulation experiments in a specific intelligent transportation case study demonstrate that EP-EI can provide energy-efficient and privacy-protecting services. © 2025 IEEE.

关键词： Differential privacy

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