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Dual Interspersion and Flexible Deployment for Few-Shot Medical Image Segmentation

IEEE Transactions on Medical Imaging 2025年 PP卷 PP页

作者： Cheng, Ziming Wang, Shidong Long, Yang Zhou, Tao Zhang, Haofeng Shao, Ling Nanjing University of Science and Technology School of Computer Science and Engineering Nanjing210094 China Newcastle University School of Engineering Newcastle upon TyneNE1 7RU United Kingdom Durham University Department of Computer Science DurhamDH1 3LE United Kingdom University of Chinese Academy of Sciences UCAS-Terminus Ai Lab Beijing100049 China

Acquiring a large volume of annotated medical data is impractical due to time, financial, and legal constraints. Consequently, few-shot medical image segmentation is increasingly emerging as a prominent research direction. Nowadays, Medical scenarios pose two major challenges: (1) intra-class variation caused by diversity among support and query sets;(2) inter-class extreme imbalance resulting from background heterogeneity. However, existing prototypical networks struggle to tackle these obstacles effectively. To this end, we propose a Dual Interspersion and Flexible Deployment (DIFD) model. Drawing inspiration from military interspersion tactics, we design the dual Interspersion module to generate representative basis prototypes from support features. These basis prototypes are then deeply interacted with query features. Furthermore, we introduce a fusion factor to fuse and refine the basis prototypes. Ultimately, we seamlessly integrate and flexibly deploy the basis prototypes to facilitate correct matching between the query features and basis prototypes, thus conducive to improving the segmentation accuracy of the model. Extensive experiments on three publicly available medical image datasets demonstrate that our model significantly outshines other SoTAs (2.78% higher dice score on average across all datasets), achieving a new level of performance. The code is available at: https://***/zmcheng9/DIFD. © 2025 IEEE.

关键词： Domain Generalization Mamba Medical Image Segmentation State Space Model

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Convolutional autoencoder-based deep learning for intracerebral hemorrhage classification using brain CT images

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Cognitive Neurodynamics 2025年第1期19卷 1-13页

作者： Nageswara Rao, B. Acharya, U. Rajendra Tan, Ru-San Dash, Pratyusa Mohapatra, Manoranjan Sabut, Sukanta Sensing and Computing Lab School of Electronics Engineering Kalinga Institute of Industrial Technology Bhubaneswar India School of Mathematics Physics and Computing University of Southern Queensland Springfield Central QLD Australia National Heart Centre Singapore Singapore Singapore Duke-NUS Medical School Singapore Singapore Department of Computer Science and Engineering Heritage Institute of Technology Kolkata India Department of Radio-Diagnosis Kalinga Institute of Medical Science Bhubaneswar India

Intracerebral haemorrhage (ICH) is a common form of stroke that affects millions of people worldwide. The incidence is associated with a high rate of mortality and morbidity. Accurate diagnosis using brain non-contrast computed tomography (NCCT) is crucial for decision-making on potentially life-saving surgery. Limited access to expert readers and inter-observer variability imposes barriers to timeous and accurate ICH diagnosis. We proposed a hybrid deep learning model for automated ICH diagnosis using NCCT images, which comprises a convolutional autoencoder (CAE) to extract features with reduced data dimensionality and a dense neural network (DNN) for classification. In order to ensure that the model generalizes to new data, we trained it using tenfold cross-validation and holdout methods. Principal component analysis (PCA) based dimensionality reduction and classification is systematically implemented for comparison. The study dataset comprises 1645 (“ICH” class) and 1648 (“Normal” class belongs to patients with non-hemorrhagic stroke) labelled images obtained from 108 patients, who had undergone CT examination on a 64-slice computed tomography scanner at Kalinga Institute of Medical sciences between 2020 and 2023. Our developed CAE-DNN hybrid model attained 99.84% accuracy, 99.69% sensitivity, 100% specificity, 100% precision, and 99.84% F1-score, which outperformed the comparator PCA-DNN model as well as the published results in the literature. In addition, using saliency maps, our CAE-DNN model can highlight areas on the images that are closely correlated with regions of ICH, which have been manually contoured by expert readers. The CAE-DNN model demonstrates the proof-of-concept for accurate ICH detection and localization, which can potentially be implemented to prioritize the treatment using NCCT images in clinical settings. © The Author(s), under exclusive licence to Springer Nature B.V. 2025.

关键词： Autoencoder CAE-DNN Deep learning ICH detection Intracerebral hemorrhage (ICH) Non-contrast CT

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UV Gaussians: Joint learning of mesh deformation and Gaussian textures for human avatar modeling

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Knowledge-Based Systems 2025年 320卷

作者： Jiang, Yujiao Liao, Qingmin Li, Xiaoyu Ma, Li Zhang, Qi Zhang, Chaopeng Lu, Zongqing Shan, Ying Shenzhen International Graduate School Tsinghua University Shenzhen518055 China Tencent AI Lab Shenzhen518054 China Department of Computer Science and Engineering the Hong Kong University of Science and Technology Hong Kong

Reconstructing photo-realistic drivable human avatars from multi-view image sequences has been a popular and challenging topic in the field of computer vision and graphics. While existing NeRF-based methods can achieve high-quality novel view rendering of human models, both training and inference processes are time-consuming. Recent approaches have utilized 3D Gaussians to represent the human body, enabling faster training and rendering. However, they undermine the importance of the mesh guidance and directly predict Gaussians in 3D space with coarse mesh guidance. This hinders the learning procedure of the Gaussians and tends to produce blurry textures. Therefore, this paper proposes UV Gaussians, which models the 3D human body by jointly learning mesh deformations and 2D UV-space Gaussian textures. The method utilizes the embedding of UV map to learn Gaussian textures in 2D space, leveraging the capabilities of powerful 2D networks to extract features. Additionally, through an independent Mesh network, the approach optimizes pose-dependent geometric deformations, thereby guiding Gaussian rendering and significantly enhancing rendering quality. A new dataset of human motion has been collected and processed, which includes multi-view images, scanned models, parametric model registration, and corresponding texture maps. Experimental results demonstrate that the proposed method achieves state-of-the-art synthesis of novel view and novel pose. The code and data will be made available as open-source. © 2025 Elsevier B.V.

关键词： Rendering (computer graphics)

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Generalizable Audio Deepfake Detection via Latent Space Refinement and Augmentation

Generalizable Audio Deepfake Detection via Latent Space Refi...

引用

International Conference on Acoustics, Speech, and Signal Processing (ICASSP)

作者： Wen Huang Yanmei Gu Zhiming Wang Huijia Zhu Yanmin Qian AI Institute Department of Computer Science and Engineering Auditory Cognition and Computational Acoustics Lab MoE Key Lab of Artificial Intelligence Shanghai Jiao Tong University Shanghai China SJTU Paris Elite Institute of Technology Ant Group Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Advances in speech synthesis technologies, like text-to-speech (TTS) and voice conversion (VC), have made detecting deepfake speech increasingly challenging. Spoofing countermeasures often struggle to generalize effectively, particularly when faced with unseen attacks. To address this, we propose a novel strategy that integrates Latent Space Refinement (LSR) and Latent Space Augmentation (LSA) to improve the generalization of deepfake detection systems. LSR introduces multiple learnable prototypes for the spoof class, refining the latent space to better capture the intricate variations within spoofed data. LSA further diversifies spoofed data representations by applying augmentation techniques directly in the latent space, enabling the model to learn a broader range of spoofing patterns. We evaluated our approach on four representative datasets, i.e. ASVspoof 2019 LA, ASVspoof 2021 LA and DF, and In-The-Wild. The results show that LSR and LSA perform well individually, and their integration achieves competitive results, matching or surpassing current state-of-the-art methods.

关键词： Deepfakes Refining Prototypes Data visualization Signal processing Robustness Data models Acoustics Text to speech

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Unsupervised Patch-GAN with Targeted Patch Ranking for Fine-Grained Novelty Detection in Medical Imaging

arXiv

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arXiv 2025年

作者： Chen, Jingkun Yang, Guang Zhang, Xiao Peng, Jingchao Zhang, Tianlu Zhang, Jianguo Han, Jungong Grau, Vicente Department of Computer Science and Engineering the Southern University of Science and Technology Shenzhen China Department of Engineer Science University of Oxford Oxford United Kingdom Department of Automation Tsinghua University Beijing China Warwick Manufacturing Group University of Warwick Coventry United Kingdom School of Information Science and Technology Northwest University Xi’an China Peng Cheng Lab Shenzhen China Guangdong Provincial Key Laboratory of Brain-inspired Intelligent Computation Shenzhen China

Detecting novel anomalies in medical imaging is challenging due to the limited availability of labeled data for rare abnormalities, which often display high variability and subtlety. This challenge is further compounded when small abnormal regions are embedded within larger normal areas, as whole-image predictions frequently overlook these subtle deviations. To address these issues, we propose an unsupervised Patch-GAN framework designed to detect and localize anomalies by capturing both local detail and global structure. Our framework first reconstructs masked images to learn fine-grained, normal-specific features, allowing for enhanced sensitivity to minor deviations from normality. By dividing these reconstructed images into patches and assessing the authenticity of each patch, our approach identifies anomalies at a more granular level, overcoming the limitations of whole-image evaluation. Additionally, a patch-ranking mechanism prioritizes regions with higher abnormal scores, reinforcing the alignment between local patch discrepancies and the global image context. Experimental results on the ISIC 2016 skin lesion and BraTS 2019 brain tumor datasets validate our framework’s effectiveness, achieving AUCs of 95.79% and 96.05%, respectively, and outperforming three state-of-the-art baselines. © 2025, CC0.

关键词： Gallium nitride

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Can A Society of Generative Agents Simulate Human Behavior and Inform Public Health Policy? A Case Study on Vaccine Hesitancy

arXiv

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arXiv 2025年

作者： Hou, Abe Bohan Du, Hongru Wang, Yichen Zhang, Jingyu Wang, Zixiao Liang, Paul Pu Khashabi, Daniel Gardner, Lauren He, Tianxing Department of Computer Science Johns Hopkins University United States Department of Civil & System Engineering Johns Hopkins University United States Department of Computer Science University of Chicago United States Department of Epidemiology Harvard University United States Media Lab and EECS Massachusetts Institute of Technology United States Institute of Interdisciplinary Information Sciences Tsinghua University China Shanghai Qi Zhi Institute China

Can we simulate a sandbox society with generative agents to model human behavior, thereby reducing the over-reliance on real human trials for assessing public policies? In this work, we investigate the feasibility of simulating health-related decision-making, using vaccine hesitancy, defined as the delay in acceptance or refusal of vaccines despite the availability of vaccination services (MacDonald, 2015), as a case study. To this end, we introduce the VACSIM1 framework with 100 generative agents powered by Large Language Models (LLMs). VACSIM simulates vaccine policy outcomes with the following steps: 1) instantiate a population of agents with demographics based on census data;2) connect the agents via a social network and model vaccine attitudes as a function of social dynamics and disease-related information;3) design and evaluate various public health interventions aimed at mitigating vaccine hesitancy. To align with real-world results, we also introduce simulation warmup and attitude modulation to adjust agents’ attitudes. We propose a series of evaluations to assess the reliability of various LLM simulations. Experiments indicate that models like Llama and Qwen can simulate aspects of human behavior but also highlight real-world alignment challenges, such as inconsistent responses with demographic profiles. This early exploration of LLM-driven simulations is not meant to serve as definitive policy guidance;instead, it serves as a call for action to examine social simulation for policy development. Copyright © 2025, The Authors. All rights reserved.

关键词： Population statistics

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SEDNet: Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution for Vehicle Re-identification

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Alexandria Engineering Journal 2025年 128卷 297-305页

作者： Mingfu Xiong Tanghao Gui Zhihong Sun Saeed Anwar Aziz Alotaibi Khan Muhammad School of Computer Science and Artificial Intelligence Wuhan Textile University Wuhan 430200 China Department of Information Security Naval University of Engineering Wuhan 430030 China The University of Western Australia (UWA) Crawley 6009 Australia Department of Computer Science College of Computing and Information Technology Taif University Taif 21974 Saudi Arabia Visual Analytics for Knowledge Laboratory (VIS2KNOW Lab) Department of Applied Artificial Intelligence College of Computing and Informatics Sungkyunkwan University Seoul 03063 South Korea

To address the issue of information redundancy (such as color and vehicle model) caused by excessive emphasis on local features in vehicle re-identification, this paper proposes a Synergistic Learning Network with Embedded Encoder and Dense Atrous Convolution (SEDNet). The proposed SEDNet framework consists of three unique branches: a global embedded multi-head encoder (GEME), local dual-dense atrous convolution (LDAC), and auxiliary attribute embedding (AAM). The GEME branch integrates the global appearance features of the vehicle to enhance consistency in descriptions from different perspectives. To suppress redundant information such as color and vehicle model information, and refine local features, the LDAC branch employs an attention mechanism to capture multiscale features using convolutional kernels with varying dilation rates. In addition, the AAM branch uses vehicle metadata, such as direction and camera perspectives, to enhance feature robustness. Our proposed SEDNet method has been rigorously tested on the mainstream benchmark vehicle re-identification datasets, including VeRi-776, VehicleID, and VeRi-Wild. The results show that our method enhances the mAP by 2.2%, 2.2%, and 0.2%, respectively, when compared to the latest methods, all evaluated on a regular scale. Additional experiments conducted on the Market-1501 and DukeMTMC-reID datasets further verify our method’s generalization capability.

关键词： Vehicle re-identification Embedded encoder Dense atrous convolution Intelligent networks Synergistic learning

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Structural and Statistical Texture Knowledge Distillation and Learning for Segmentation

arXiv

引用

arXiv 2025年

作者： Ji, Deyi Zhao, Feng Lu, Hongtao Wu, Feng Ye, Jieping MoE Key Laboratory of Brain-inspired Intelligent Perception and Cognition University of Science and Technology of China China Department of Computer Science and Engineering MOE Key Lab of Artificial Intelligence AI Institute Shanghai Jiao Tong University China Alibaba Group China

Low-level texture feature/knowledge is also of vital importance for characterizing the local structural pattern and global statistical properties, such as boundary, smoothness, regularity, and color contrast, which may not be well addressed by high-level deep features. In this paper, we aim to re-emphasize the low-level texture information in deep networks for semantic segmentation and related knowledge distillation tasks. To this end, we take full advantage of both structural and statistical texture knowledge and propose a novel Structural and Statistical Texture Knowledge Distillation (SSTKD) framework for semantic segmentation. Specifically, Contourlet Decomposition Module (CDM) is introduced to decompose the low-level features with iterative Laplacian pyramid and directional filter bank to mine the structural texture knowledge, and Texture Intensity Equalization Module (TIEM) is designed to extract and enhance the statistical texture knowledge with the corresponding Quantization Congruence Loss (QDL). Moreover, we propose the Co-occurrence TIEM (C-TIEM) and generic segmentation frameworks, namely STLNet++ and U-SSNet, to enable existing segmentation networks to harvest the structural and statistical texture information more effectively. Extensive experimental results on three segmentation tasks demonstrate the effectiveness of the proposed methods and their state-of-the-art performance on seven popular benchmark datasets, respectively. Copyright © 2025, The Authors. All rights reserved.

关键词： Semantic Segmentation

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The Power of Graph Signal Processing for Chip Placement Acceleration

arXiv

引用

arXiv 2025年

作者： Liu, Yiting Zhou, Hai Wang, Jia Yang, Fan Zeng, Xuan Shang, Li School of Computer Science Fudan University Shanghai China Department of ECE Northwestern University Evanston United States Department of ECE IIIinois Institute of Technology Chicago United States State Key Lab of ASIC & System Microelectronics Department Fudan University Shanghai China

Placement is a critical task with high computation complexity in VLSI physical design. Modern analytical placers formulate the placement objective as a nonlinear optimization task, which suffers a long iteration time. To accelerate and enhance the placement process, recent studies have turned to deep learning-based approaches, particularly leveraging graph convolution networks (GCNs). However, learning-based placers require time- and data-consuming model training due to the complexity of circuit placement that involves large-scale cells and design-specific graph statistics. This paper proposes GiFt, a parameter-free technique for accelerating placement, rooted in graph signal processing. GiFt excels at capturing multi-resolution smooth signals of circuit graphs to generate optimized placement solutions without the need for time-consuming model training, and meanwhile significantly reduces the number of iterations required by analytical placers. Experimental results show that GiFt significantly improving placement efficiency, while achieving competitive or superior performance compared to state-of-the-art placers. In particular, compared to DREAMPlace, the recently proposed GPU-accelerated analytical placer, GF-Placer improves total runtime over 45%. Copyright © 2025, The Authors. All rights reserved.

关键词： Nonlinear programming

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Large Scale Finite-Temperature Real-time Time Dependent Density Functional Theory Calculation with Hybrid Functional on ARM and GPU Systems

arXiv

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arXiv 2025年

作者： Liu, Rongrong Guo, Zhuoqiang Sha, Qiuchen Zhao, Tong Li, Haibo Hu, Wei Liu, Lijun Tan, Guangming Jia, Weile State Key Lab of Processors Institute of Computing Technology Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China School of Mathematics and Statistics The University of Melbourne Melbourne Australia School of Computer Science and Technology University of Science and Technology of China Hefei China Department of Mechanical Engineering Graduate School of Engineering Osaka University Osaka Japan

Ultra-fast electronic phenomena originating from finite temperature, such as nonlinear optical excitation, can be simulated with high fidelity via real-time time dependent density functional theory (rt-TDDFT) calculations with hybrid functional. However, previous rt-TDDFT simulations of real materials using the optimal gauge-known as the parallel transport gauge-have been limited to low-temperature systems with band gaps. In this paper, we introduce the parallel transport-implicit midpoint (PT-IM) method, which significantly accelerates finite-temperature rt-TDDFT calculations of real materials with hybrid function. We first implement PT-IM with hybrid functional in our plane wave code PWDFT, and optimized it on both GPU and ARM platforms to build a solid baseline code. Next, we propose a diagonalization method to reduce computation and communication complexity, and then, we employ adaptively compressed exchange (ACE) method to reduce the frequency of the most expensive Fock exchange operator. Finally, we adopt the ring_based method and the shared memory mechanism to overlap computation and communication and alleviate memory consumption respectively. Numerical results show that our optimized code can reach 3072 atoms for rt-TDDFT simulation with hybrid functional at finite temperature on 192 computing nodes, the time-to-solution for one time step is 429.3s, which is 41.4 times faster compared to the baseline. © 2025, CC BY-NC-SA.

关键词： Graphics processing unit

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