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Hierarchical Spatiotemporal Attention Network for Fine-grained Brain Cognitive State Recognition

Hierarchical Spatiotemporal Attention Network for Fine-grain...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Yike Wu Ning An Zixuan Zeng YouYong Kong School of Computer Science and Engineering Southeast University Nanjing Ministry of Education Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Applications(Southeast University) China School of Software Engineering Southeast University Nanjing

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Brain cognitive state recognition based on functional Magnetic Resonance Imaging(fMRI) can capture brain functional activities under different tasks and help understand the neural mechanisms of the brain, which has always been one of the focuses of neuroscience research. Different from the prediction of the brain cognitive domain, the prediction of brain fine-grained cognitive state is based on each moment in the process of executing the task. Therefore, it is necessary to extract more fine-grained effective information. Existing studies focus on modeling and classifying the complete time series, ignoring the brain activity state at each time. So we propose a hierarchical spatiotemporal attention network(FineBrainNet) to recognize fine-grained brain cognitive state. Guided by coarse-grained cognitive domain labels, we trained different sub-modules for fine-grained states under each cognitive domain to capture relevant cognitive state changes more accurately in specific task. Extensive experiments on the HCP-Task dataset show that FineBrainNet can achieve accurate prediction of fine-grained brain cognitive state.

关键词： Accuracy Neuroscience image recognition Time series analysis Magnetic resonance Speech recognition signal processing Brain modeling Spatiotemporal phenomena Speech processing

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Clinically Robust Polyp Segmentation: Enhanced Generalization and Perturbation Resistance

Clinically Robust Polyp Segmentation: Enhanced Generalizatio...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Shanchuan Wang Fang Yuan Jian-Nan Su Min Gan College of Computer Science and Technology Qingdao University Qingdao China College of Computer and Data Science Fuzhou University Fuzhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Colonoscopy is vital for detecting colorectal polyps, which are closely linked to colorectal cancer. Accurate segmentation of polyps in colonoscopic images is essential for diagnosis and surgical planning but is challenging due to variability in polyp size, shape, and unclear boundaries. The Segment Anything Model (SAM) has shown promise in polyp segmentation but relies heavily on user-provided prompts and involves a large number of parameters, limiting its practicality in clinical settings. To address the limitations of SAM in clinical practice, we introduced Low Rank and Perturbation Segment Anything Model (LP-SAM) to improve segmentation accuracy and generalization ability while reducing the parameter count and complexity of user input. LP-SAM showed enhanced generalization and a lightweight design, making it more suitable for clinical applications where precise user input may not always be feasible. Comparative evaluations demonstrate that LP-SAM outperforms state-of-the-art methods on datasets such as CVC-ColonDB, CVC-300, and ETIS.

关键词： Resistance Training image segmentation Adaptation models Accuracy Shape Perturbation methods Surgery Planning Speech processing

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JANE: Joint Angle Networks Assisting 3D Human Pose Estimation

JANE: Joint Angle Networks Assisting 3D Human Pose Estimatio...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Jinhuan Wang Yuzhen Zhao xujie Song Wenzhou Chen Qi xuan Institute of Cyberspace Security College of Information Engineering Zhejiang University of Technology HangZhou China Department of Computer Science and Technology Hangzhou Dianzi University HangZhou China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

3D human pose estimation (HPE) is crucial due to its extensive applications. While current 3D HPE methods focus on human skeleton topology for accuracy, they often overlook joint angle information, which is vital in 2D-to-3D pose lifting. This paper introduces the Joint Angle Network (JANE) model to leverage joint angle features from 2D poses. We propose an angle graph convolution module to extract joint angle features and a multi-stage parallel fusion module to integrate these features with input image data. Experiments on Human3.6M show that our method improves accuracy by 8.7% and 2.6% over non-spatiotemporal baselines using 2D ground truth and 2D pose detectors, respectively.

关键词： Visualization Solid modeling Three-dimensional displays Accuracy Convolution Pose estimation Feature extraction Transformers Topology Speech processing

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Col-OLHTR: A Novel Framework for Multimodal Online Handwritten Text Recognition

Col-OLHTR: A Novel Framework for Multimodal Online Handwritt...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Chenyu Liu Jinshui Hu Baocai Yin Jia Pan Bing Yin Jun Du Qingfeng Liu University of Science and Technology of China Hefei China iFLYTEK Research Hefei China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Online Handwritten Text Recognition (OLHTR) has gained considerable attention for its diverse range of applications. Current approaches usually treat OLHTR as a sequence recognition task, employing either a single trajectory or image encoder, or multi-stream encoders, combined with a CTC or attention-based recognition decoder. However, these approaches face several drawbacks: 1) single encoders typically focus on either local trajectories or visual regions, lacking the ability to dynamically capture relevant global features in challenging cases; 2) multi-stream encoders, while more comprehensive, suffer from complex structures and increased inference costs. To tackle this, we propose a Collaborative learning-based OLHTR framework, called Col-OLHTR, that learns multimodal features during training while maintaining a single-stream inference process. Col-OLHTR consists of a trajectory encoder, a Point-to-Spatial Alignment (P2SA) module, and an attention-based decoder. The P2SA module is designed to learn image-level spatial features through trajectory-encoded features and 2D rotary position embeddings. During training, an additional image-stream encoder-decoder is collaboratively trained to provide supervision for P2SA features. At inference, the extra streams are discarded, and only the P2SA module is used and merged before the decoder, simplifying the process while preserving high performance. Extensive experimental results on several OLHTR benchmarks demonstrate the state-of-the-art (SOTA) performance, proving the effectiveness and robustness of our design.

关键词： Training Handwriting recognition Visualization image recognition Text recognition Speech recognition Trajectory Decoding Speech processing Streams

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UV-Mamba: A DCN-Enhanced State Space Model for Urban Village Boundary Identification in High-Resolution Remote Sensing images

UV-Mamba: A DCN-Enhanced State Space Model for Urban Village...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Lulin Li Ben Chen xuechao Zou Junliang xing Pin Tao School of Computer Technology and Applications Qinghai University Xining China School of Computer Science and Technology Beijing Jiaotong University Beijing China Department of Computer Science and Technology Tsinghua University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Due to the diverse geographical environments, intricate landscapes, and high-density settlements, the automatic identification of urban village boundaries using remote sensing images remains a highly challenging task. This paper proposes a novel and efficient neural network model called UV-Mamba for accurate boundary detection in high-resolution remote sensing images. UV-Mamba mitigates the memory loss problem in lengthy sequence modeling, which arises in state space models (SSM) with increasing image size, by incorporating deformable convolutions (DCN). Its architecture utilizes an encoder-decoder framework and includes an encoder with four deformable state space augmentation (DSSA) blocks for efficient multi-level semantic extraction and a decoder to integrate the extracted semantic information. We conducted experiments on two large datasets showing that UV-Mamba achieves state-of-the-art performance. Specifically, our model achieves 73.3% and 78.1% IoU on the Beijing and xi’an datasets, respectively, representing improvements of 1.2% and 3.4% IoU over the previous best model while also being 6× faster in inference speed and 40× smaller in parameter count. Source code and pre-trained models are available at https://***/Devin-Egber/UV-Mamba.

关键词： Deformable models Location awareness image segmentation Source coding Semantics Neural networks Data mining Sustainable development Speech processing Remote sensing

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A Novel Self-Supervised Contrastive Learning Framework for Masked EEG Motor imagery Modeling

A Novel Self-Supervised Contrastive Learning Framework for M...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Kunkun Zhang Qianwei Zhou Haigen Hu College of Computer Science and Technology Zhejiang University of Technology Hangzhou PR China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Electroencephalography (EEG) is vital for brain-computer interfaces (BCIs) due to its non-invasive approach and high temporal resolution data capabilities, amid challenges such as data scarcity and the need for extensive labeling. Significant inter-individual variability in EEG signals further limits model generalization. Concurrently, the use of self-supervised pre-training, particularly through masked modeling, is gaining traction in time series analysis to mitigate labeling costs. Although this method involves reconstructing masked signal from unmasked series, random masking can disrupt critical temporal variations, complicating effective representation learning. We thus introduce SSL-MEMI, a novel self-supervised contrastive learning framework for masked EEG motor imagery modeling, integrating Domain Adaptive Alignment (DAA) and Multi-View Temporal-spatial Attention module (MTSA) to effectively handle EEG variability. This framework utilizes manifold-based masking to reconstruct original sequences from masked series, thereby enhancing classification accuracy. When tested on the BCI Competition IV and High Gamma datasets, SSL-MEMI outperforms existing methods, achieving top accuracies and demonstrating superior domain adaptation through reduced Global ${\mathcal{A}}$-distance scores. This study advances EEG classification and indicates broader applications for self-supervised learning in biomedical signal processing. The source code is available at https://***/KunKun-Zhang/***.

关键词： Manifolds Adaptation models Accuracy Time series analysis Contrastive learning Brain modeling Motors Electroencephalography Labeling image reconstruction

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Deep Sylvester Posterior Inference for Adaptive Compressed Sensing in Ultrasound Imaging

Deep Sylvester Posterior Inference for Adaptive Compressed S...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Simon W. Penninga Hans van Gorp Ruud J.G. van Sloun Department of Electrical Engineering Eindhoven University of Technology Eindhoven The Netherlands

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Ultrasound images are commonly formed by sequential acquisition of beam-steered scan-lines. Minimizing the number of required scan-lines can significantly enhance frame rate, field of view, energy efficiency, and data transfer speeds. Existing approaches typically use static subsampling schemes in combination with sparsity-based or, more recently, deep-learning-based recovery. In this work, we introduce an adaptive subsampling method that maximizes intrinsic information gain in-situ, employing a Sylvester Normalizing Flow encoder to infer an approximate Bayesian posterior under partial observation in real-time. Using the Bayesian posterior and a deep generative model for future observations, we determine the subsampling scheme that maximizes the mutual information between the subsampled observations, and the next frame of the video. We evaluate our approach using the EchoNet cardiac ultrasound video dataset and demonstrate that our active sampling method outperforms competitive baselines, including uniform and variable-density random sampling, as well as equidistantly spaced scanlines, improving mean absolute reconstruction error by 15%. Moreover, posterior inference and the sampling scheme generation are performed in just 0.015 seconds (66Hz), making it fast enough for real-time 2D ultrasound imaging applications.

关键词： Adaptation models Ultrasonic imaging Imaging Streaming media signal processing Real-time systems Bayes methods Speech processing Mutual information image reconstruction

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Guess What I Think: Streamlined EEG-to-image Generation with Latent Diffusion Models

Guess What I Think: Streamlined EEG-to-Image Generation with...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Eleonora Lopez Luigi Sigillo Federica Colonnese Massimo Panella Danilo Comminiello Dept. Information Engineering Electronics and Telecommunications (DIET) Sapienza University of Rome Italy

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Generating images from brain waves is gaining increasing attention due to its potential to advance brain-computer interface (BCI) systems by understanding how brain signals encode visual cues. Most of the literature has focused on fMRI-to-image tasks as fMRI is characterized by high spatial resolution. However, fMRI is an expensive neuroimaging modality and does not allow for real-time BCI. On the other hand, electroencephalography (EEG) is a low-cost, non-invasive, and portable neuroimaging technique, making it an attractive option for future real-time applications. Nevertheless, EEG presents inherent challenges due to its low spatial resolution and susceptibility to noise and artifacts, which makes generating images from EEG more difficult. In this paper, we address these problems with a streamlined framework based on the ControlNet adapter for conditioning a latent diffusion model (LDM) through EEG signals. We conduct experiments and ablation studies on popular benchmarks to demonstrate that the proposed method beats other state-of-the-art models. Unlike these methods, which often require extensive preprocessing, pretraining, different losses, and captioning models, our approach is efficient and straightforward, requiring only minimal preprocessing and a few components. The code is available at https://***/LuigiSigillo/GWIT.

关键词： Neuroimaging Adaptation models Visualization Streaming media Functional magnetic resonance imaging Diffusion models Brain modeling Electroencephalography Real-time systems Spatial resolution

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CGNet: Classification-Guided Multi-Task Interactive Network for Hyperspectral and Multispectral image Fusion

CGNet: Classification-Guided Multi-Task Interactive Network ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： Guohua Lv Yanlong xu Yongbiao Gao Guixin Zhao Guotao Wang xiangcheng Sun Key Laboratory of Computing Power Network and Information Security Ministry of Education Shandong Computer Science Center (National Supercomputer Center in Jinan) Qilu University of Technology (Shandong Academy of Sciences) China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science China Shandong Key Laboratory of Ubiquitous Intelligent Computing China Key Laboratory of New Generation Artificial Intelligence Technology and Its Interdisciplinary Application (Southeast University) Ministry of Education China Beihang University Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The goal of fusing hyperspectral images (HSI) and multispectral images (MSI) is to generate high-resolution hyperspectral images for downstream tasks. However, most existing methods overlook the specific requirements of these tasks, leading to a gap between the fusion process and its subsequent applications due to insufficient guidance from downstream tasks. To address this issue, we propose a classification-guided multitask interactive network (CGNet) that integrates both fusion and classification tasks into a unified framework, with two branches producing the fused image and classification results, respectively. In the fusion branch, we design a multi-level residual refinement module to efficiently integrate spatial and spectral information. Additionally, an attention-based multi-scale fusion module, incorporating both spatial and channel attention, is carefully crafted to enhance representation learning. In the classification branch, both 2-D and 3-D convolutions are employed to improve classification performance. Moreover, an information interaction module is proposed to guide the fusion task based on classification outcomes. Extensive experiments demonstrate that our method outperforms state-of-the-art approaches on the Pavia Centre and Pavia University datasets.

关键词： Representation learning Three-dimensional displays Attention mechanisms Convolution Streaming media Multitasking Speech processing image fusion Information exchange Hyperspectral imaging

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CTGDiff: A Conditional Diffusion Model for Cardiotocography signal Synthesis

CTGDiff: A Conditional Diffusion Model for Cardiotocography ...

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International conference on Acoustics, Speech, and signal processing (ICASSP)

作者： xiaoqing Li Pufan Cai Yu Lu Shijie Shi Liangkun Ma xianghua Fu College of Big Data and Internet Shenzhen Technology University Shenzhen China National Engineering Laboratory for Big Data System Computing Technology Shenzhen University Shenzhen China Department of Surgery Yong Loo Lin School of Medicine National University of Singapore Singapore School of Applied Technology Shenzhen University Shenzhen China Department of Obstetrics and Gynecology Peking Union Medical College Hospital Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

The analysis of Cardiotocography (CTG) signals is often hindered by challenges such as limited data availability and label imbalance, which can undermine the performance of deep learning models. To address these issues, we present CTGDiff, a novel conditional diffusion model designed for generating synthetic Fetal Heart Rate (FHR) and Uterine Contraction (UC) signals. CTGDiff leverages both Phase-Rectified signal Averaging (PRSA) spectrograms and UC as conditioning inputs for FHR, and integrates time encoding, condition generation from PRSA features, and residual blocks with dilated convolutions to capture both temporal dynamics and long-range dependencies. Extensive experiments, both qualitative and quantitative, demonstrate the model’s ability to synthesize high-quality CTG signals. In comparison with GANs and image-based diffusion models, CTGDiff achieves superior signal fidelity and distribution similarity for FHR, as indicated by metrics such as a 0.004 maximum mean deviation (MMD), 0.646 percent root mean square difference (PRD), 3.951 relative entropy (RE), and 0.291 Frechet distance (FD). Expert evaluations confirm that the model can generate both normal and abnormal CTG signals with high accuracy, conditioned on specific input data. These results underscore the potential of diffusion models for a wide range of applications in biomedical time series analysis, including signal synthesis, imputation, and noise reduction.

关键词： Measurement Fetal heart rate Biological system modeling Time series analysis Diffusion models Data models signal synthesis Cardiography Speech processing Spectrogram

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