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arXiv 2024年

作者： Chin, Seungbeom Ryu, Junghee Kim, Yong-Su Okinawa Institute of Science and Technology Graduate University Okinawa904-0495 Japan Department of Electrical and Computer Engineering Sungkyunkwan University Suwon16419 Korea Republic of Center for Quantum Information R&D Korea Institute of Science and Technology Information Daejeon34141 Korea Republic of Division of Quantum Information KISTI School Korea University of Science and Technology Daejeon34141 Korea Republic of Seoul02792 Korea Republic of Division of Quantum Information Technology KIST School Korea University of Science and Technology Seoul02792 Korea Republic of

High-dimensional multipartite entanglement plays a crucial role in quantum information science. However, existing schemes for generating such entanglement become complex and costly as the dimension of quantum units increases. In this work, we overcome the limitation by proposing a significantly enhanced linear optical heralded scheme that generates the d-level N-partite GHZ state with single-photon sources and linear operations. Our scheme requires dN photons, which is the minimal required photon number, with substantially improved success probability from previous schemes. It employs linear optical logic gates compatible with any qudit encoding system and can generate generalized GHZ states with installments of beamsplitters. With efficient generations of high-dimensional resource states, our work opens avenues for further exploration in high-dimensional quantum information processing. © 2024, CC BY-NC-ND.

关键词： Photons

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Noninvasive Tracking of Every Individual in Unmarked Mouse Groups Using Multi-Camera Fusion and Deep Learning

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Neuroscience Bulletin 2023年第6期39卷 893-910页

作者： Feng Su Yangzhen Wang Mengping Wei Chong Wang Shaoli Wang Lei Yang Jianmin Li Peijiang Yuan Dong-Gen Luo Chen Zhang Department of Neurobiology School of Basic Medical SciencesBeijing Key Laboratory of Neural Regeneration and RepairCapital Medical UniversityBeijing100069China Chinese Institute for Brain Research Beijing102206China State Key Laboratory of Translational Medicine and Innovative Drug Development Nanjing210000China Peking-Tsinghua Center for Life Sciences Academy for Advanced Interdisciplinary StudiesPeking UniversityBeijing100871China School of Life Sciences Tsinghua UniversityBeijing100084China School of Biological Science and Medical Engineering Beihang UniversityBeijing100191China The Key Laboratory of Developmental Genes and Human Disease Institute of Life SciencesSoutheast UniversityNanjing210096JiangsuChina Institute for Artificial Intelligence the State Key Laboratory of Intelligence Technology and SystemsBeijing National Research Center for Information Science and TechnologyDepartment of Computer Science and TechnologyTsinghua UniversityBeijing100084China School of Mechanical Engineering and Automation Beihang UniversityBeijing100191China

Accurate and efficient methods for identifying and tracking each animal in a group are needed to study complex behaviors and social *** tracking methods(e.g.,marking each animal with dye or surgically implanting microchips)can be invasive and may have an impact on the social behavior being *** overcome these shortcomings,video-based methods for tracking unmarked animals,such as fruit flies and zebrafish,have been ***,tracking individual mice in a group remains a challenging problem because of their flexible body and complicated interaction *** this study,we report the development of a multi-object tracker for mice that uses the Faster region-based convolutional neural network(R-CNN)deep learning algorithm with geometric transformations in combination with multi-camera/multi-image fusion *** system successfully tracked every individual in groups of unmarked mice and was applied to investigate chasing *** proposed system constitutes a step forward in the noninvasive tracking of individual mice engaged in social behavior.

关键词： Noninvasive tracking Deep learning Multi-camera Mouse group Social interaction

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Safe Optimal Interactions Between Automated and Human-Driven Vehicles in Mixed Traffic with Event-triggered Control Barrier Functions

arXiv

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arXiv 2023年

作者： Li, Anni Cassandras, Christos G. Xiao, Wei Division of Systems Engineering Center for Information and Systems Engineering Boston University BrooklineMA02446 United States Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology United States

This paper studies safe driving interactions between Human-Driven Vehicles (HDVs) and Connected and Automated Vehicles (CAVs) in mixed traffic where the dynamics and control policies of HDVs are unknown and hard to predict. In order to address this challenge, we employ event-triggered Control Barrier Functions (CBFs) to estimate the HDV model online, construct data-driven and state-feedback safety controllers, and transform constrained optimal control problems for CAVs into a sequence of event-triggered quadratic programs. We show that we can ensure collision-free between HDVs and CAVs and demonstrate the robustness and flexibility of our framework on different types of human drivers in lane-changing scenarios while guaranteeing safety with human-in-the-loop interactions. Copyright © 2023, The Authors. All rights reserved.

关键词： Vehicles

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Learning Feasibility Constraints for Control Barrier Functions

arXiv

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arXiv 2023年

作者： Xiao, Wei Cassandras, Christos G. Belta, Calin A. The Computer Science and Artificial Intelligence Lab Massachusetts Institute of Technology United States The Division of Systems Engineering Center for Information and Systems Engineering Boston University BrooklineMA02446 United States

It has been shown that optimizing quadratic costs while stabilizing affine control systems to desired (sets of) states subject to state and control constraints can be reduced to a sequence of Quadratic Programs (QPs) by using Control Barrier Functions (CBFs) and Control Lyapunov Functions (CLFs). In this paper, we employ machine learning techniques to ensure the feasibility of these QPs, which is a challenging problem, especially for high relative degree constraints where High Order CBFs (HOCBFs) are required. To this end, we propose a sampling-based learning approach to learn a new feasibility constraint for CBFs;this constraint is then enforced by another HOCBF added to the QPs. The accuracy of the learned feasibility constraint is recursively improved by a recurrent training algorithm. We demonstrate the advantages of the proposed learning approach to constrained optimal control problems with specific focus on a robot control problem and on autonomous driving in an unknown environment. Copyright © 2023, The Authors. All rights reserved.

关键词： Lyapunov functions

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Fundus image Quality Assessment and Enhancement: a Systematic Review

arXiv

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arXiv 2025年

作者： Li, Heng Li, Haojin Ou, Mingyang Yu, Xiangyang Zhang, Xiaoqing Niu, Ke Fu, Huazhu Liu, Jiang Research Institute of Trustworthy Autonomous Systems SUSTech Shenzhen China Department of Computer Science and Engineering SUSTech Shenzhen China Center for High Performance Computing and Shenzhen Key Laboratory of Intelligent Bioinformatics Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen China Computer School Beijing Information Science and Technology University Beijing China Singapore

As an affordable and convenient eye scan, fundus photography holds the potential for preventing vision impairment, especially in resource-limited regions. However, fundus image degradation is common under intricate imaging environments, impacting following diagnosis and treatment. Consequently, image quality assessment (IQA) and enhancement (IQE) are essential for ensuring the clinical value and reliability of fundus images. While existing reviews offer some overview of this field, a comprehensive analysis of the interplay between IQA and IQE, along with their clinical deployment challenges, is lacking. This paper addresses this gap by providing a thorough review of fundus IQA and IQE algorithms, research advancements, and practical applications. We outline the fundamentals of the fundus photography imaging system and the associated interferences, and then systematically summarize the paradigms in fundus IQA and IQE. Furthermore, we discuss the practical challenges and solutions in deploying IQA and IQE, as well as offer insights into potential future research directions. Copyright © 2025, The Authors. All rights reserved.

关键词： Photography

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Positional Encoding-Guided Transformer-Based Multiple Instance Learning for Histopathology Whole Slide images Classification

SSRN

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SSRN 2024年

作者： Shi, Jun Sun, Dongdong Wu, Kun Jiang, Zhiguo Kong, Xue Wang, Wei Wu, Haibo Zheng, Yushan School of Software Hefei University of Technology Anhui Province Hefei230601 China School of Computer Science and Information Engineering Hefei University of Technology Anhui Province Hefei230601 China Beijing102206 China Department of Pathology the First Affiliated Hospital of USTC Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China Intelligent Pathology Institute Division of Life Sciences and Medicine University of Science and Technology of China Anhui Province Hefei230036 China School of Engineering Medicine Beijing Advanced Innovation Center for Biomedical Engineering Beihang University Beijing100191 China Beijing Advanced Innovation Center for Biomedical Engineering China

Whole slide image (WSI) classification is of great clinical significance in computer-aided pathological diagnosis. Due to the high cost of manual annotation, weakly supervised WSI classification methods have gained more attention. As the most representative, multiple instance learning (MIL) generally aggregates the predictions or features of the patches within a WSI to achieve the slidelevel classification under the weak supervision of WSI labels. However, most existing MIL methods ignore spatial position relationships of the patches which is likely to strengthen the discriminative ability of WSI-level features. In this paper, we propose a novel position encoding-guided transformerbased multiple instance learning (PEGTB-MIL) method for histopathology WSI classification. It aims to encode the spatial positional property of the patch into its corresponding semantic features and explore the potential correlation among the patches for improving the WSI classification performance. Concretely, the deep features of the patches in WSI are first extracted and simultaneously a position encoder is used to encode the spatial 2D positional information of the patches into the spatialaware features. After incorporating the semantic features and spatial embeddings, multi-head selfattention (MHSA) is applied to explore the contextual and spatial dependencies of the fused features. Particularly, we introduce an auxiliary reconstruction task to enhance the spatial-semantic consistency and generalization ability of features. The proposed method is evaluated on one public TCGA dataset and two in-house datasets. Experimental results validate it is effective in the tasks of lung cancer subtyping and gene mutation status prediction for lung cancer and gastrointestinal stromal tumor (GIST), and superior to the state-of-the-art MIL-based methods. © 2024, The Authors. All rights reserved.

关键词： Forecasting

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Multi-modal Long-Short Distance Attention-based Transformer-GAN for PET Reconstruction with Auxiliary MRI

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IEEE Transactions on Circuits and Systems for Video technology 2025年

作者： Zeng, Pinxian Zeng, Xinyi Wang, Yan Zhou, Luping Zu, Chen Wu, Xi Zhou, Jiliu Shen, Dinggang Sichuan University School of Computer Science China University of Sydney School of Electrical and Information Engineering Australia Department of Risk Controlling Research *** China Chengdu University of Information Technology School of Computer Science China ShanghaiTech University School of Biomedical Engineering State Key Laboratory of Advanced Medical Materials and Devices Shanghai201210 China Shanghai United Imaging Intelligence Co. Ltd Shanghai200232 China Shanghai Clinical Research and Trial Center Shanghai201210 China

To obtain high-quality PET scans while minimizing potential radiation hazards for patients, various GAN-based methods have been developed to reconstruct high-quality standard-count PET (SPET) images from low-count PET (LPET) ones. While recent efforts try to integrate MRI or CT to enhance reconstruction in a multi-modal way, current architectures mainly face two limitations: (1) CNN backbones or simple Transformer bottleneck layers are insufficient for robust semantic understanding, and (2) the identical strategies for multi-modal feature extraction and fusion overlook each modality's respective importance for the reconstruction task. In this work, we propose the Multi-modal Long-Short Distance Attention-based Transformer-GAN (MLSDA-GAN), a novel network combining 3D transformer and CNN architecture for PET image reconstruction. Specifically, to extract fine-grained features with a small number of parameters, our MLSDA-GAN integrates multi-scale convolution into the embedding part of the transformer. As for our multi-modal design, given the strong correlation between LPET and SPET in structural characteristics, we treat MRI as an auxiliary modality to LPET and achieve effective multi-modal extraction and fusion strategies. These strategies include (1) a PET-specific Self-attention Extraction (PSE) block for comprehensive feature extraction of the primary LPET and (2) a Multi-modality Cross-attention Fusion (MCF) block for effective multi-modal interaction and fusion, enabling us to more efficiently model both long- and short-range relationships in the corresponding feature extraction and fusion processes. Experiments demonstrate superiority of our method quantitatively and qualitatively. Code is available at https://***/Aru321/MLSDA-GAN. © 1991-2012 IEEE.

关键词： Radiation hazards

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Back to the Future: Challenges of Sparse and Irregular Medical image Time Series

Back to the Future: Challenges of Sparse and Irregular M...

引用

Workshop on Longitudinal Disease Tracking and Modeling with Medical images and Data, LDTM 2024, 5th International Workshop on Multiscale Multimodal Medical Imaging, MMMI 2024, 1st Workshop on Machine Learning for Multimodal/-sensor Healthcare Data, ML4MHD2024 and Workshop on Multimodal Learning and Fusion Across Scales for Clinical Decision Support, ML-CDS 2024 held in conjunction with the 27th International Conference on Medical image Computing and computer Assisted Intervention, MICCAI 2024

作者： Disch, Nico Albert Peretzke, Robin Roy, Saikat Ulrich, Constantin Zimmerer, David Stiefelhagen, Rainer Kleesiek, Jens Maier-Hein, Klaus Division of Medical Image Computing German Cancer Research Center Heidelberg Germany HIDSS4Health - Helmholtz Information and Data Science School for Health Karlsruhe Heidelberg Germany Faculty of Mathematics and Computer Science University of Heidelberg Heidelberg Germany Pattern Analysis and Learning Group Department of Radiation Oncology Heidelberg University Hospital Heidelberg Germany Karlsruhe Institute of Technology Karlsruhe Germany University Hospital Essen Essen Germany University Hospital Essen West German Cancer Center Essen Essen Germany Medical Faculty Heidelberg University of Heidelberg Heidelberg Germany

ISBN: (纸本)9783031845246

In longitudinal medical image analysis, most work focuses on regularly sampled images, or on tasks like regression or classification. However, in the clinical context, images are frequently generated irregularly due to factors such as cost constraints. This work tackles the problem of irregularly sampled longitudinal medical imaging by evaluating methods in segmentation and reconstruction tasks. We examine the reconstruction for real-life MRI from patients with Alzheimer’s Disease (AD), where the model predicts future MRI scans from an arbitrary number of input images sampled at irregular intervals. However, experiments show that most models cannot surpass the performance of using an image from the same patient at a different time point as a baseline. Therefore, we conducted experiments on a synthetic dataset to better isolate effects in temporal learning. These experiments offer insights into model behavior and serve as a benchmark for validating models’ longitudinal learning capability, thus providing a straightforward method to assess temporal understanding in a controlled environment. © The Author(s), under exclusive license to Springer Nature Switzerland AG 2025.

关键词： Neurodegenerative diseases

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Context-CAM: Context-Level Weight-Based CAM with Sequential Denoising to Generate High-Quality Class Activation Maps

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IEEE Transactions on image Processing 2025年 34卷 3431-3446页

作者： Du, Jie Chen, Wenbing Vong, Chi-Man Liu, Peng Wang, Tianfu Shenzhen University National-Regional Key Technology Engineering Laboratory for Medical Ultrasound Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging School of Biomedical Engineering Medical School Guangdong Shenzhen 518060 China University of Macau Department of Computer and Information Science 999078 Macao University of Electronic Science and Technology of China Artificial Intelligence Industrial Innovation Research Center Shenzhen Institute for Advanced Study Shenzhen 518110 China

Class activation mapping (CAM) methods have garnered considerable research attention because they can be used to interpret the decision-making of deep convolutional neural network (CNN) models and provide initial masks for weakly supervised semantic segmentation (WSSS) tasks. However, the class activation maps generated by most CAM methods usually have two limitations: 1) a lack of the ability to cover the whole object when using low-level features;and 2) introducing background noise. To mitigate these issues, an innovative Context-level weights-based CAM (Context-CAM) method is proposed, which guarantees: 1) the non-discriminative regions that have similar appearances and are located close to the discriminative regions can also be highlighted by the newly designed Region-Enhanced Mapping (REM) module using context-level weights;and 2) the background noises are gradually eliminated via a newly proposed Semantic-guided Reverse Sequence Fusion (SRSF) strategy that can sequentially denoise and fuse the region-enhanced maps from the last layer to the first layer. Extensive experimental results show that our Context-CAM can generate higher-quality class activation maps than classic and state-of-the-art (SOTA) CAM methods in terms of the Energy-Based Pointing Game (EBPG) score, and the improvements are up to 35.49% when compared to the second-best method. Moreover, for WSSS tasks, our Context-CAM can directly replace the CAM method used in existing WSSS methods without any architectural modification to further improve the segmentation performance. © 1992-2012 IEEE.

关键词： Class activation mapping context-level weights map fusion weakly supervised semantic segmentation

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Underwater image Enhancement Utilizing Adaptive Color Correction and Model Conversion for Dehazing

SSRN

引用

SSRN 2023年

作者： Li, Yiming Li, Daoyu Gao, Zhijie Wang, Shuai Jiao, Qiang Bian, Liheng School of Information and Electronics Advanced Research Institute of Multidisciplinary Science Beijing Institute of Technology Beijing100081 China Center for Strategic Research on Frontier and Interdisciplinary Engineering Science and Technology Beijing Institute of Technology Beijing100081 China School of Computer Science and Engineering Beihang University Beijing100083 China Ministry of Public Security Information and Communication Center Beijing100741 China

Underwater imaging is typically affected by absorption and scattering, which can lead to color distortion and image blurring. However, no universal color constancy algorithm for correcting underwater images exists due to the wide variation in underwater scenes. Furthermore, methods based on learning to improve underwater image quality often suffer from a lack of ground truth, which can compromise the validity and authenticity of restored images. In this study, we propose an adaptive underwater image enhancement technique that corrects image color and reduces haze using a valid dataset with ground truth. Specifically, we first use hue channel statistics from underwater images to build a dataset of color-corrected images from different underwater scenes. We use this dataset to train an Unet-like network for adaptive color correction. After color correction, underwater images often exhibit characteristics similar to hazy terrestrial images. We refer to this phenomenon as "model conversion". The hazy terrestrial images have corresponding ground truth. Therefore, we train a Transformer-like network with hazy terrestrial image datasets to remove haze in underwater images. Our method is more robust for different underwater scenes and elegantly solves the problem of lacking ground truth. A series of experiments demonstrate that our method achieves superior performance compared to state-of-the-art methods in terms of both visual quality and quantitative metrics. © 2023, The Authors. All rights reserved.

关键词： Underwater imaging

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