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检索条件"任意字段=IEEE Symposium on Computational Intelligence in Image and Signal Processing"

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MDN: Mamba-Driven Dualstream Network For Medical Hyperspectral image Segmentation

MDN: Mamba-Driven Dualstream Network For Medical Hyperspectr...

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

作者： Shijie Lin Boxiang Yun Wei Shen Qingli Li Anqiang Yang Yan Wang Shanghai Key Laboratory of Multidimensional Information Processing East China Normal University Shanghai China Key Laboratory of Artificial Intelligence Ministry of Education Shanghai Jiao Tong University Shanghai China Department of Pathology Changning Maternal and Infant Health Hospital Shanghai China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Medical Hyperspectral Imaging (MHSI) offers potential for computational pathology and precision medicine. However, existing CNN and Transformer struggle to balance segmentation accuracy and speed due to high spatial-spectral dimensionality. In this study, we leverage Mamba’s global context modeling to propose a dual-stream architecture for joint spatial-spectral feature extraction. To address the limitation of Mamba’s unidirectional aggregation, we introduce a recurrent spectral sequence representation to capture low-redundancy global spectral features. Experiments on a public Multi-Dimensional Choledoch dataset and a private Cervical Cancer dataset show that our method outperforms state-of-the-art approaches in segmentation accuracy while minimizing resource usage and achieving the fastest inference speed. Our code will be available at https://***/DeepMed-Lab-ECNU/MDN.

关键词： image segmentation Accuracy Precision medicine Redundancy signal processing Transformers Speech processing Hyperspectral imaging Biomedical imaging Context modeling

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Exploring Parallelism in ZynqNet AI Engine to Accelerate image Classification in Aerospace

Exploring Parallelism in ZynqNet AI Engine to Accelerate Ima...

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ieee Latin American symposium on Circuits and Systems (LASCAS)

作者： Eduardo Marañon Aguilar Fabio Benevenuti Fernanda Lima Kastensmidt Federal University of Rio Grande do Sul (UFRGS) Institute of Informatics Microelectronics Graduate Program (PGMICRO) Porto Alegre Brazil

ISBN: (数字)9798331522124

ISBN: (纸本)9798331522131

This research investigates various configurations of the Zynqnet AI Engine to accelerate image classification tasks using the SAT-6 dataset and a 4-layer CNN. The C++ code for the hardware accelerator was translated into RTL using Vivado HLS and integrated with the Zynq processing System on a ZedBoard Zynq-7000 ARM/FPGA SoC. The AI engine was configured to work with 32 -bit floating point, 8bit fixed point and 8 -bit integer type operations, with different processing elements for parallelizing CNN tasks. Given a 64-bit data memory bus in communication with the accelerator and an 8-bit integer quantization, the variation in the number of processing elements does not result in a significant performance improvement. To address this, 8 values were grouped for reading and writing in 64-bit parallel mode, and different communication architectures were designed to improve data transfer between the accelerator and DDR memory, with optional enablement of L1D and L2 cache. The research analyzes CNN computation performance and FPGA resource usage (LUTs, FFs, BRAMs, DSPs) across configurations, providing insights into the trade-offs in FPGA-based CNN implementations, aiding the development of efficient, high-performance hardware accelerators for Deep Learning applications.

关键词： Quantization (signal) Writing Table lookup System-on-chip Convolutional neural networks Artificial intelligence Hardware acceleration Engines Field programmable gate arrays image classification

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image Test Libraries for the In-Field Test of Ultra-Low-Power Devices

Image Test Libraries for the In-Field Test of Ultra-Low-Powe...

引用

Latin American Test Workshop, LATW

作者： Antonio Porsia Giacomo Perlo Annachiara Ruospo Ernesto Sanchez Politecnico di Torino DAUIN Turin Italy

ISBN: (数字)9781665477635

ISBN: (纸本)9781665477642

In recent years, research and technology advancements have driven exponential growth in the adoption of Artificial intelligence (AI)-based systems, even in safety-critical contexts such as autonomous driving and healthcare applications. The joint effort of academia and industry has yielded techniques and standards with the objective of ensuring the safe operation of AI-based technology. In the specific context of Convolutional Neural Networks (CNNs) running on GPUs, image Test Libraries (ITLs) have been proposed as an effective method for performing on-line functional testing of GPU multipliers. This is achieved by launching the inference of a set of test images containing a set of ATPG-generated functional test patterns. However, while the demand for computational power for DNN models is constantly increasing, another branch of Machine Learning (ML) research, namely TinyML, focuses on minimizing the computational requirements of DNN models in order to bring AI capabilities to edge devices, whose constraints on power usage, memory space and processing power do not allow for the deployment of conventional DNN models. This research work aims to adapt the ITL technique to CNNs running on ultra-low-power edge hardware, while also overcoming some limitations of GPU ITLs. Experimental results demonstrate that a single test image generated using the proposed method is capable of detecting 96.01% of stuck-at faults occurring in the 32-bit integer multiplier of a RISC-V-based ultra-low-power System-on-Chip executing a quantized CNN.

关键词： Adaptation models computational modeling image edge detection Tiny machine learning Neural networks Graphics processing units Libraries System-on-chip Convolutional neural networks Testing

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CognoSpeak: an Automatic, Remote Assessment of Early Cognitive Decline in Real-World Conversational Speech

CognoSpeak: an Automatic, Remote Assessment of Early Cogniti...

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computational intelligence in Health and Medicine (CIHM), ieee symposium on

作者： Madhurananda Pahar Fuxiang Tao Bahman Mirheidari Nathan Pevy Rebecca Bright Swapnil Gadgil Lise Sproson Dorota Braun Caitlin Illingworth Daniel Blackburn Heidi Christensen School of Computer Science University of Sheffield Sheffield UK Therapy Box London UK NIHR Devices for Dignity HTC Sheffield Teaching Hospitals NHS Foundation Trust Sheffield UK Sheffield Institute for Translational Neuroscience (SITraN) University of Sheffield Sheffield UK

ISBN: (数字)9798331508333

ISBN: (纸本)9798331508340

The early signs of cognitive decline are often noticeable in conversational speech, and identifying those signs is crucial in dealing with later and more serious stages of neurodegenerative diseases. Clinical detection is costly and time-consuming and although there has been recent progress in the automatic detection of speech-based cues, those systems are trained on relatively small databases, lacking detailed metadata and demographic information. This paper presents CognoSpeak and its associated data collection efforts. CognoSpeak asks memory-probing long and short-term questions and administers standard cognitive tasks such as verbal and semantic fluency and picture description using a virtual agent on a mobile or web platform. In addition, it collects multimodal data such as audio and video along with a rich set of metadata from primary and secondary care, memory clinics and remote settings like people's homes. Here, we present results from 126 subjects whose audio was manually transcribed. Several classic classifiers, as well as large language model-based classifiers, have been investigated and evaluated across the different types of prompts. We demonstrate a high level of performance; in particular, we achieved an $F_{1}$ -score of 0.873 using a DistilBERT model to discriminate people with cognitive impairment (dementia and people with mild cognitive impairment (MCI)) from healthy volunteers using the memory responses, fluency tasks and cookie theft picture description. CognoSpeak is an automatic, remote, low-cost, repeatable, non-invasive and less stressful alternative to existing clinical cognitive assessments.

关键词： Training Semantics Speech recognition Metadata Linguistics signal processing Feature extraction Speech processing Standards Dementia

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VLIMNet: A Visible Light And Infrared image Matching Network Based On Segment Anything Model And SuperPoint

VLIMNet: A Visible Light And Infrared Image Matching Network...

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

作者： Zhongyuan Chen Zhan Zhang Decheng Zuo Ning Wang Liufeng Fan Zhiwei Liu Faculty of Computing Harbin Institute of Technology Harbin China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

This paper introduces a novel method for matching visible light and infrared images, termed the Visible Light and Infrared image Matching Network (VLIMNet). In the image encoding stage, we incorporate a generative architecture-based modality transformation network after the SuperPoint encoder, enabling the local geometric features extracted from infrared images to more closely resemble those of visible light images. This reduces the impact of modality differences. Simultaneously, we utilize the image encoder of the Segment Anything Model to obtain global semantic descriptors. During the decoding stage, we fuse the global semantic descriptors with the local geometric descriptors and perform joint decoding to obtain keypoints and new feature vectors. The matching process is computed by solving a differentiable optimal transport problem based on the LightGlue network, a graph neural network with an attention mechanism. Compared to other matching models, our approach demonstrates improvements across various metrics in the domain of infrared and visible light image matching, particularly excelling in matching images with significant pose differences. Specifically, our method achieves approximately a 5% improvement in matching accuracy compared to the highest accuracy matching methods.

关键词： image segmentation Accuracy Fuses image matching computational modeling Semantics Feature extraction Graph neural networks Vectors Decoding

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MFT: Modal Fusion Transformer for Cross-Modal Fusion in 3D Object Detection

MFT: Modal Fusion Transformer for Cross-Modal Fusion in 3D O...

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

作者： Haojie Cai Dongfu Yin Fei Yu Siting Xiong Guangdong Laboratory of Artificial Intelligence and Digital Economy (SZ) Shenzhen University Shenzhen China College of Computer Science and Software Engineering Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

Increasing attention has been garnered by LiDAR points and multi-view images fusion based on Transformer for supplementing another modality in 3D object detection. However, challenges persist for cross-modal fusion methods due to the heterogeneity of these two modalities, leading to issues such as inaccurate detection results encountered by Transformer-based methods. In this work, a one-way mid-level fusion based framework for 3D object detection named Modal Fusion Transformer (MFT) using LiDAR points and multi-view images is introduced. It comprises a Depth-Guided Generation(DGG) module, Position Encoding Generation (PEG) module and Cross Modal Fusion(CMF) module. Specifically, depth information from point cloud is utilized for both gathering the image depth map and initializing object queries in DGG. PEG unifies the form of position encoding from LiDAR features and multi-view image features. Depth and position information of object from images is aggregated to point clouds by CMF, which fully explores dual-modal information. Furthermore, a Modal Fusion Network with deformable attention named fast-MFT is introduced to reduce the relatively large computational cost associated with global attention. Our MFT and fast-MFT achieve competitive performance while maintaining a faster inference speed than other models.

关键词： Point cloud compression Three-dimensional displays Laser radar image coding Object detection signal processing Transformers Encoding Speech processing image fusion

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DARNet: A Dual Attention Residual Network for Medical image Classification

DARNet: A Dual Attention Residual Network for Medical Image ...

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

作者： Ao Zhang Zhenghua Guan Tengda Zhang Wenzheng Hu Yi Liu Baiying Lei School of Biomedical Engineering Shenzhen University Shenzhen China Artificial Intelligence Shenzhen University Shenzhen China

ISBN: (数字)9798350368741

ISBN: (纸本)9798350368758

In the field of medical image analysis, accurate classification of images is crucial for diagnosing diseases and formulating treatment plans. Many studies have shown that global features and local features help reduce noise interference in medical images. Due to the fixed receptive field size of the convolution kernel, it is difficult to capture the global features of the image. Although the self-attention-based Transformer can model long-range dependencies, it has high computational complexity and lacks local inductive bias. This paper proposes a new module based on dual attention, the Dual Attention Residual Module(DARNet), which uses multi-head self-attention (MHSA) to enhance the global feature extraction ability of convolutional neural network (CNN), while the convolutional block attention module (CBAM) enhances local feature extraction, and then fuses features at different levels through dual-step attention fusion (DSAF). Then we use the architecture of resnet to build the network-Dual Attention Residual Network (DARNet). We evaluate our network on the ISIC2018 and Kvasir datasets and demonstrate its superior performance compared with state-of-the-art models

关键词： Visualization Accuracy Convolution Computer architecture Feature extraction Convolutional neural networks Medical diagnostic imaging Residual neural networks image classification Diseases

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2009 ieee symposium on computational intelligence in image processing, CIIP 2009 - processing

2009 IEEE Symposium on Computational Intelligence in Image P...

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2009 ieee symposium on computational intelligence in image processing, CIIP 2009

ISBN: (纸本)9781424427604

The proceedings contain 19 papers. The topics discussed include: adaptive lambda-enhancement: type I versus type II fuzzy implementation;an automated GA-based fuzzy image enhancement method;an efficient architecture for hardware implementations of image processing algorithms;inter-modality registration of NMRi and histological section images using neural networks regression in Gabor feature space;2D ultrasound image segmentation using graph cuts and local image features;contextual classification of high-resolution satellite images;a modified fuzzy C-means algorithm with adaptive spatial information for color image segmentation;3-D reconstruction and automatic fusion of edge maps from different modalities of an object;hybridization of particle swarm optimization with the K-means algorithm for image classification;and metric planar rectification from perspective view via circles.

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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 ...

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2025 ieee International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Wang, Ziyu Pan, Tengyu Li, Zhenyu Wu, Ji Li, Xiuxing Wang, Jianyong 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: (纸本)9798350368741

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. © 2025 ieee.

关键词： computational neuroscience multi-modal learning region of interest analysis small sample learning

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DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or Mono-planar 2D X-ray(s)

DX2CT: Diffusion Model for 3D CT Reconstruction from Bi or M...

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2025 ieee International Conference on Acoustics, Speech, and signal processing, ICASSP 2025

作者： Jeong, Yun Su Yoo, Hye Bin Chun, Il Yong Department of Electrical and Computer Engineering Sungkyunkwan University Korea Republic of Departments of Artificial Intelligence and Advanced Display Engineering Sungkyunkwan University Korea Republic of Suwon16419 Korea Republic of

ISBN: (纸本)9798350368741

computational tomography (CT) provides high-resolution medical imaging, but it can expose patients to high radiation. X-ray scanners have low radiation exposure, but their resolutions are low. This paper proposes a new conditional diffusion model, DX2CT, that reconstructs three-dimensional (3D) CT volumes from bi or mono-planar X-ray image(s). Proposed DX2CT consists of two key components: 1) modulating feature maps extracted from two-dimensional (2D) X-ray(s) with 3D positions of CT volume using a new transformer and 2) effectively using the modulated 3D position-aware feature maps as conditions of DX2CT. In particular, the proposed transformer can provide conditions with rich information of a target CT slice to the conditional diffusion model, enabling high-quality CT reconstruction. Our experiments with the bi or mono-planar X-ray(s) benchmark datasets show that proposed DX2CT outperforms several state-of-the-art methods. Our codes and model will be available at: https://***/intyeger/DX2CT. © 2025 ieee.

关键词： Computed tomography (CT) Diffusion models Three-dimensional (3D) reconstruction X-ray radiography

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