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检索条件"任意字段=Conference on Image Reconstruction from Incomplete Data"

共 14740 条记录，以下是241-250 订阅

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TROI: Cross-Subject Pretraining with Sparse Voxel Selection for Enhanced fMRI Visual Decoding

TROI: Cross-Subject Pretraining with Sparse Voxel Selection ...

引用

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

作者： Ziyu Wang Tengyu Pan Zhenyu Li Ji Wu Xiuxing Li Jianyong Wang Department of Computer Science and Technology Tsinghua University Beijing China School of Computer Science and Engineering Beihang University Beijing China School of Computer Science and Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

fMRI (functional Magnetic Resonance Imaging) visual decoding involves decoding the original image from brain signals elicited by visual stimuli. This often relies on manually labeled ROIs (Regions of Interest) to select brain voxels. However, these ROIs can contain redundant information and noise, reducing decoding performance. Additionally, the lack of automated ROI labeling methods hinders the practical application of fMRI visual decoding technology, especially for new subjects. This work presents TROI (Trainable Region of Interest), a novel two-stage, data-driven ROI labeling method for cross-subject fMRI decoding tasks, particularly when subject samples are limited. TROI leverages labeled ROIs in the dataset to pretrain an image decoding backbone on a cross-subject dataset, enabling efficient optimization of the input layer for new subjects without retraining the entire model from scratch. In the first stage, we introduce a voxel selection method that combines sparse mask training and low-pass filtering to quickly generate the voxel mask and determine input layer dimensions. In the second stage, we apply a learning rate rewinding strategy to fine-tune the input layer for downstream tasks. Experimental results on the same small sample dataset as the baseline method for brain visual retrieval and reconstruction tasks show that our voxel selection method surpasses the state-of-the-art method MindEye2 with an annotated ROI mask.

关键词： Training Visualization Noise Training data Functional magnetic resonance imaging Brain modeling Decoding Labeling Speech processing Optimization

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

作者： Xianchao Guan Zheng Zhang Yifeng Wang Ye Zhang Danling Jiang Yongbing Zhang Pengcheng Laboratory Shenzhen China Harbin Institute of Technology Shenzhen China Department of Gastroenterology Peking University Shenzhen Hospital Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Immunohistochemistry (IHC) examination is essential for determining breast cancer subtypes and provides critical prognostic factors to guide treatment decisions. However, the complex and expensive preparation of IHC staining limits its widespread use in clinical practice. Recent advancements in generative models have introduced virtual staining as a promising alternative, yet obtaining pixel-level paired data in clinical settings remains a significant challenge. In this paper, we propose Multi-IHC Net, which utilizes unpaired data to simultaneously generate Ki67, ER, PR, and HER2 images from H&E-stained breast tissue. Specifically, a general encoder extracts generalized features from H&E images, while interactive decoders reconstruct the four types of IHC images. Additionally, a feature alignment module also models the correlations among the different IHC stains. To enhance accuracy, we introduce a pathology consistency mechanism between H&E and adjacent IHC images. Extensive experiments demonstrate the superiority of our method compared to state-of-the-art approaches.

关键词： Pathology Correlation Accuracy Breast tissue Signal processing Feature extraction Breast cancer Decoding Speech processing image reconstruction

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Robust Phase Retrieval from Quantized and Noisy Measurements

Robust Phase Retrieval from Quantized and Noisy Measurements

引用

Acoustics, Speech, and Signal Processing Workshops (ICASSPW), IEEE International conference on

作者： Yihong Gao Luteng Zhu Zhuoyang An Mingjie Shao Department of Electrical and Electronic Engineering The University of Hong Kong Hong Kong SAR China School of Information Science and Engineering Shandong University Qingdao China Computer School Beijing Information Science and Technology University Beijing China Key Laboratory of System and Control AMSS Chinese Academy of Sciences Beijing China

ISBN: (数字)9798331519315

ISBN: (纸本)9798331519322

The phase retrieval (PR) problem involves recovering a signal from amplitude-only measurements and has been extensively studied in various physical measurement and signal processing systems. Recently, there has been increasing interest in the quantized phase retrieval (QPR) problem, where the observed amplitude is quantized to binary values. The existing studies have focused on the noiseless case. In this paper, we address a robust QPR problem that accounts for both quantization and noise. We formulate the robust QPR problem using maximum-likelihood estimation (MLE), which results in a non-convex and challenging optimization problem due to the complexity of its likelihood function. To obtain a high-quality solution, we develop an expectation-maximization (EM) algorithm combined with the Wirtinger flow, allowing us to convert the problem into a more manageable form at each iteration. Our method is implemented on both randomly generated synthetic data and image reconstruction tasks. The results demonstrate that our approach significantly enhances signal recovery performance under the combined effects of quantization and noise. Additionally, the proposed method exhibits better runtime efficiency compared to existing approaches.

关键词： Maximum likelihood estimation Phase measurement Quantization (signal) Runtime Noise Signal processing algorithms Noise measurement Speech processing Optimization Synthetic data

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Multi-modal Dynamic Point Cloud Geometric Compression Based on Bidirectional Recurrent Scene Flow*

Multi-modal Dynamic Point Cloud Geometric Compression Based ...

引用

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

作者： Fangzhe Nan Frederick Li Zhuoyue Wang Gary K.L. Tam Zhaoyi Jiang DongZheng DongZheng Bailin Yang School of Statistics and Mathematics Zhejiang Gongshang University School of Computer Science and Technolgy Zhejiang Gongshang University University of Durham University of California Berkeley Swansea University Universal Ubiquitous Technology Co. LTD

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Deep learning methods have recently shown significant promise in compressing the geometric features of point clouds. However, challenges arise when consecutive point clouds contain holes, resulting in incomplete information that complicates motion estimation. To our knowledge, most existing dynamic point cloud compression methods have largely overlooked this critical issue. Moreover, these methods typically employ a multi-scale single-pass approach for motion estimation, performing only one estimation at each scale. This limits accuracy and adversely impacts compression performance. To address these challenges, we propose a dynamic point cloud compression model called M2BR-DPCC (Multi-Modal Multi-Scale Bidirectional Recursion for Dynamic Point Cloud Compression). Our method introduces two key innovations. First, we integrate both point cloud and image data as inputs, leveraging a multi-modal feature representation completion (MFRepC) approach to align information across modalities. This addresses the issue of missing data in point clouds by using complementary information from images. Second, we implement a multi-scale bidirectional recursive (MSBR) motion estimation method. This module iteratively refines motion flows in both forward and backward directions, progressively enhancing point cloud features and improving motion estimation accuracy. Experimental results on widely used datasets, including MVUB and 8iVFB, demonstrate the effectiveness of our approach. Compared to existing methods, M2BR-DPCC achieves superior performance, with an average BD-rate improvement of 95.23% over V-PCC, 12.92% over D-DPCC, and 16.16% over patchDPCC. These results underscore the potential of leveraging multi-modal data and bidirectional refinement for dynamic point cloud compression.

关键词： Point cloud compression Technological innovation image coding Accuracy Limiting Motion estimation Dynamics Estimation Filling Speech processing

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SSM2Mel: State Space Model to Reconstruct Mel Spectrogram from the EEG

SSM2Mel: State Space Model to Reconstruct Mel Spectrogram fr...

引用

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

作者： Cunhang Fan Sheng Zhang Jingjing Zhang Zexu Pan Zhao Lv School of Computer Science and Technology Anhui University Hefei China Alibaba Group Singapore

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Decoding speech from brain signals is a challenging research problem that holds significant importance for studying speech processing in the brain. Although breakthroughs have been made in reconstructing the mel spectrograms of audio stimuli perceived by subjects at the word or letter level using non-invasive electroencephalography (EEG), there is still a critical gap in precisely reconstructing continuous speech features, especially at the minute level. To address this issue, this paper proposes a State Space Model (SSM) to reconstruct the mel spectrogram of continuous speech from EEG, named SSM2Mel. This model introduces a novel Mamba module to effectively model the long sequence of EEG signals for imagined speech. In the SSM2Mel model, the S4-UNet structure is used to enhance the extraction of local features of EEG signals, and the Embedding Strength Modulator (ESM) module is used to incorporate subject-specific information. Experimental results show that our model achieves a Pearson correlation of 0.069 on the SparrKULee dataset, which is a 38% improvement over the previous baseline.

关键词： Neuroscience Modulation Brain modeling Feature extraction Electroencephalography Decoding data mining Speech processing image reconstruction Spectrogram

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Few-Shot Arbitrary Super-Resolution Considering Consistency and Realness

Few-Shot Arbitrary Super-Resolution Considering Consistency ...

引用

International conference on Electronics, Information and Communications (ICEIC)

作者： Joonhee Lee Junseo Bang Se Young Chun Department of Electrical and Computer Engineering INMC & IPAI Seoul National University Seoul Republic of Korea

ISBN: (数字)9798331510756

ISBN: (纸本)9798331510763

Super-resolution (SR) is the task of reconstructing visually natural high-resolution (HR) images from low-resolution (LR) images. This field has made significant advancements with the development of deep learning, and recent research has focused on arbitrary-scale super-resolution, where a single model performs upsampling across continuous scales. However, most existing studies on arbitrary-scale super-resolution have relied on supervised learning models, which require large amounts of data and annotations. We propose a novel approach that demonstrates performance equal to or better than existing methods while utilizing fewer data, thus opening up new possibilities in this domain. This method also presents the potential for personalization, allowing the model to be adjusted according to individual user preferences or requirements. Specifically, we propose a method that integrates a few-shot arbitrary super-resolution upsampler trained with limited data into a zero-shot learning framework for super-resolution based on existing Diffusion Posterior Sampling (DPS) techniques. Additionally, this approach addresses the issues of insufficient detail representation in traditional regression-based SR methods and the lack of fidelity to the original images observed in generative-based SR methods. To achieve this, we employ a parallel diffusion model that uses DPS in both spatial and Fourier domains to simultaneously generate two types of images while maintaining consistency and realness. As a result, our method achieves performance in both PSNR and LPIPS that is competitive with supervised learning-based regression and generative SR methods.

关键词： Visualization Annotations Superresolution Zero shot learning Supervised learning Diffusion models data models Spatial resolution Few shot learning image reconstruction

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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...

引用

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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MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estimation by Planar-Parallax Geometry in Automotive Applications

MonoPP: Metric-Scaled Self-Supervised Monocular Depth Estima...

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IEEE Workshop on Applications of Computer Vision (WACV)

作者： Gasser Elazab Torben Gräber Michael Unterreiner Olaf Hellwich CARIAD SE Technische Universität Berlin

ISBN: (数字)9798331510831

ISBN: (纸本)9798331510848

Self-supervised monocular depth estimation (MDE) has gained popularity for obtaining depth predictions directly from videos. However, these methods often produce scaleinvariant results, unless additional training signals are provided. Addressing this challenge, we introduce a novel selfsupervised metric-scaled MDE model that requires only monocular video data and the camera's mounting position, both of which are readily available in modern vehicles. Our approach leverages planar-parallax geometry to reconstruct scene structure. The full pipeline consists of three main networks, a multi-frame network, a singleframe network, and a pose network. The multi-frame network processes sequential frames to estimate the structure of the static scene using planar-parallax geometry and the camera mounting position. Based on this reconstruction, it acts as a teacher, distilling knowledge such as scale information, masked drivable area, metric-scale depth for the static scene, and dynamic object mask to the singleframe network. It also aids the pose network in predicting a metric-scaled relative pose between two subsequent images. Our method achieved state-of-the-art results for the driving benchmark KITTI for metric-scaled depth prediction. Notably, it is one of the first methods to produce self-supervised metric-scaled depth prediction for the challenging Cityscapes dataset, demonstrating its effectiveness and versatility. Project page: https://***/

关键词： Geometry Training Computer vision Depth measurement Automotive applications Cameras Vehicle dynamics image reconstruction Videos Visual perception

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7th International conference on Applied Informatics, ICAI 2024

7th International Conference on Applied Informatics, ICAI 20...

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7th International conference on Applied Informatics, ICAI 2024

The proceedings contain 39 papers. The special focus in this conference is on Applied Informatics. The topics include: Automatic Identification of Forest Areas in the "Carolina" Park Using Res...

ISBN: (纸本)9783031751462

The proceedings contain 39 papers. The special focus in this conference is on Applied Informatics. The topics include: Automatic Identification of Forest Areas in the "Carolina" Park Using ResNet50, EfficientNetB0 and VGG16: A Case Study;deep Convolutional Neural Network for Autonomic Function Estimation in Intensive Care Patients;deep Learning Techniques for Oral Cancer Detection: Enhancing Clinical Diagnosis by ResNet and DenseNet Performance;machine Learning Operations Applied to Development and Model Provisioning;MultiMF: A Deep Multimodal Academic Resources Recommendation System;netScribed: A Deep Learning Approach for Machine-Based Melody Transcription of Audio Files;Safeguarding Patient data: Advanced Security in Wireless Body Area Networks and AI-Driven Healthcare Systems;YOLO Convolutional Neural Network for Building Damage Detection in Hydrometeorological Disasters Using Satellite imagery;extraction and Selection of Multi-omic Features for the Breast Cancer Survival Prediction;migration from On-Premises to Cloud: Challenges and Opportunities;improvement of Acute and Chronic Nutritional Status by Supplying a Metabolic Biostimulator to Children in High Risk of Malnutrition. Introducing a Technological Platform to Enable Automatic Analyses and Reporting;relationship between Demographic, Geographical and Access to Health Services Factors;RGB image reconstruction for Precision Agriculture: A Systematic Literature Review;Developing AI-Driven Cross-Platform Geolocation for Enhanced Strategic Decision-Making;from data to Decisions: Performance Evaluation of Retail Recommender Systems;Optimizing Fraud Detection in Traffic Accident Insurance Claims Through AI Models: Strategies and Challenges;Code Legends: RPG Game as a Support Tool for Programming in High School;development of Mental Agility Mini-games in a Video Game for Early Detection of Mild Cognitive Impairment: An Innovative Approach in Mental Health.

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7th International conference on Applied Informatics, ICAI 2024

7th International Conference on Applied Informatics, ICAI 20...

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7th International conference on Applied Informatics, ICAI 2024

The proceedings contain 39 papers. The special focus in this conference is on Applied Informatics. The topics include: Automatic Identification of Forest Areas in the "Carolina" Park Using Res...

ISBN: (纸本)9783031751431

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