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7th International Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

7th International Workshop on Predictive Intelligence in Med...

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7th International Workshop on Predictive Intelligence in Medicine, PRIME 2024, held in conjunction with the 27th International conference on Medical Image computing and Computer Assisted Intervention, MICCAI 2024

ISBN: (纸本)9783031745607

The proceedings contain 17 papers. The special focus in this conference is on Predictive Intelligence in Medicine. The topics include: Spectral Graph Sample Weighting for Interpretable Sub-cohort Analysis in Predictive models for Neuroimaging;RCT: Relational Connectivity Transformer for Enhanced Prediction of Absolute and Residual Intelligence;gene-to-Image: Decoding Brain Images from Genetics via Latent Diffusion models;physics-Guided Multi-view Graph neural network for Schizophrenia Classification via Structural-Functional Coupling;automated Patient-Specific Pneumoperitoneum Model Reconstruction for Surgical Navigation Systems in Distal Gastrectomy;MNA-net: Multimodal Neuroimaging Attention-Based Architecture for Cognitive Decline Prediction;Improving Brain MRI Segmentation with Multi-Stage Deep Domain Unlearning;DynGNN: Dynamic Memory-Enhanced Generative GNNs for Predicting Temporal Brain Connectivity;Strongly Topology-Preserving GNNs for Brain Graph Super-Resolution;generative Hypergraph neural network for Multiview Brain Connectivity Fusion;identifying Brain Ageing Trajectories Using Variational Autoencoders with Regression Model in Neuroimaging Data Stratified by Sex and Validated Against Dementia-Related Risk Factors;integrating Deep Learning with Fundus and optical Coherence Tomography for Cardiovascular Disease Prediction;self-Supervised Contrastive Learning for Consistent Few-Shot Image Representations;Neurocognitive Latent Space Regularization for Multi-label Diagnosis from MRI;Segmentation of Brain Metastases in MRI: A Two-Stage Deep Learning Approach with Modality Impact Study.

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An Automatic Video Tag Extraction Method based on Large Language Model Text Content Parsing 7

An Automatic Video Tag Extraction Method based on Large Lang...

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7th International Conference on Data Science and Information Technology, DSIT 2024

作者： Chen, Kai Hu, Min Hangzhou Dianzi University School Of Management Hangzhou China Beijing Zhongke Ratu Technology Co. Ltd AI Laboratory Beijing China

ISBN: (纸本)9798350384093

In the information age, short videos have become an integral part of daily life, leading to an exponential increase in the volume of video data on short video platforms. Without effective organization and classification of these video resources, management becomes a significant challenge. Therefore, the development of efficient video categorization techniques is crucial. This study proposes an automatic video tag extraction method based on Large Language Model text content parsing (VTE-LLM). The method integrates OCR (optical Character Recognition) and CRNN (Convolutional Recurrent neural network) models to extract subtitle information from videos, and utilizes Large Language models for text parsing to automatically generate video classifications and tags. This approach effectively addresses the cost and efficiency issues associated with traditional methods. Experimental results demonstrate that the proposed method achieves high accuracy across multiple video types. © 2024 IEEE.

关键词： Convolutional neural networks

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Research on emotion classification of multimodal physiological signals based on cross group convolution neural network

Research on emotion classification of multimodal physiologic...

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2024 International Conference on Image, Signal Processing, and Pattern Recognition, ISPP 2024

作者： Zhuan, Zhikun Bian, Yan Zhang, Zhiwen Wang, Yuanchao Yang, Yang Geng, Liqing Sun, Miao Tianjin Key Laboratory of Information Sensing and Intelligent Control Tianjin University of Technology and Education Tianjin300222 China Beijing University of Technology Beijing100124 China

ISBN: (纸本)9781510680425

Emotion recognition from physiological signals is a crucial area in affective computing. However, traditional CNN models face challenges in accuracy and efficiency. This paper proposes a lightweight IGC-CNN model that integrates interleaved group convolutions with the LeNet-5 network. Experimental results using EEG, EMG, and EDA signals collected across happiness, sadness, and fear states show that IGC-CNN achieves an average accuracy of 94.74%, outperforming traditional CNNs by 10.06%. Statistical analysis confirms the significance of this improvement (P © 2024 SPIE.

关键词： Convolutional neural networks

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Extensions for compiler infrastructure for neural networks to support automated generation of high-performance tensor programs for DSPs 9

Extensions for compiler infrastructure for neural networks t...

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9th International Symposium on Advances in Electrical, Electronics, and Computer Engineering, ISAEECE 2024

作者： Huang, Rui Li, Zhe Ji, Yuxiong Yang, Jing Wang, Yaohua College of Computer National University of Defense Technology Changsha410073 China

ISBN: (纸本)9781510683303

To widely apply deep learning technology, it is necessary to deploy neural network models on different hardware platforms. Multi-core Digital Signal Processors (DSPs), as one of these platforms, exhibit the potential to accelerate deep learning in specific applications and scenarios due to their energy efficiency advantages. CINN, as an AI compiler, is been proposed for deploying DNN models on diverse hardware devices. However, due to differences in hardware architectures, there is a risk of poor utilization of memory and computing resources, leading to increased execution time. In this work, we propose several extensions to achieve optimization specific to DSPs, and allow CINN to automatically generate high-performance tensor programs for our DSPs through a search approach. Experimental result shows that there is a significant improvement in execution performance for the automatically generated tensor operators containing reduction operation compared to manual schedule. © 2024 SPIE.

关键词： Program compilers

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Mixed Precision Quantized neural network Accelerator for Remote Sensing Images Classification 16

Mixed Precision Quantized Neural Network Accelerator for Rem...

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16th IEEE International Conference on Electronic Measurement and Instruments, ICEMI 2023

作者： Zhang, Haijun Yao, Bowen Shao, Wenyi Xu, Xiuwen Liu, Liansheng Peng, Yu School of Electronics and Information Engineering Harbin Institute of Technology Harbin China Shanghai Institute of Satellite Engineering Shanghai China

ISBN: (纸本)9798350327144

The application of convolutional neural network (CNN) for classifying optical remote sensing images holds great significance in areas such as maritime management, environmental protection, and more. However, the significant computational demands of CNN pose obstacles to the efficient deployment at the edge. Quantization techniques, as effective model compression methods, have been widely used to reduce computational demands during model deployment. Current uniform low-precision quantization methods suffer from significant accuracy loss. Mixed-precision quantization compresses models while maintaining accuracy, but it introduces computations with multiple bit widths. Due to the introduction of multiple bit-width computations, general-purpose computing platforms are unable to leverage the computational efficiency of quantized network. In this article, a Zynq SoC-based mixed-precision neural network accelerator design is proposed, which can fully leverage its energy efficiency. Experimental results show that the mixed-precision neural network accelerator achieves 4.96 × improvement in inference speed compared with the baseline. © 2023 IEEE.

关键词： Convolutional neural networks

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Infusing Physically Inspired Known Operators in Deep models of Ultrasound Elastography 26th

Infusing Physically Inspired Known Operators in Deep Models ...

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26th International Conference on Medical Image computing and Computer-Assisted Intervention (MICCAI)

作者： Tehrani, Ali K. Z. Rivaz, Hassan Concordia Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9783031439063;9783031439070

The displacement estimation step of Ultrasound Elastography (USE) can be done by optical flow Convolutional neural networks (CNN). Even though displacement estimation in USE and computer vision share some challenges, USE displacement estimation has two distinct characteristics that set it apart from the computer vision counterpart: high-frequency nature of RF data, and the physical rules that govern the motion pattern. The high-frequency nature of RF data has been well addressed in recent works by modifying the architecture of the available optical flow CNNs. However, insufficient attention has been placed on the integration of physical laws of deformation into the displacement estimation. In USE, lateral displacement estimation, which is highly required for elasticity and Poisson's ratio imaging, is a more challenging task compared to the axial one since the motion in the lateral direction is limited, and the sampling frequency is much lower than the axial one. Recently, Physically Inspired ConstrainT for Unsupervised Regularized Elastography (PICTURE) has been introduced which incorporates the physical laws of deformation by introducing a regularized loss function. PICTURE tries to limit the range of the lateral displacement by the feasible range of Poisson's ratio and the estimated high-quality axial displacement. Despite the improvement, the regularization was only applied during the training phase. Furthermore, only a feasible range for Poisson's ratio was enforced. We exploit the concept of known operators to incorporate iterative refinement optimization methods into the network architecture so that the network is forced to remain within the physically plausible displacement manifold. The refinement optimization methods are embedded into the different pyramid levels of the network architecture to improve the estimate. Our results on experimental phantom and in vivo data show that the proposed method substantially improves the estimated displacements.

关键词： Ultrasound Elastography Convolutional neural networks Physically inspired constraint Known operator Poisson's ratio

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Research on the Combination of Blockchain and Training of Deep neural network Model

Research on the Combination of Blockchain and Training of De...

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2022 International Conference on Computer, Artificial Intelligence, and Control Engineering, CAICE 2022

作者： Shen, Yunke Zhang, Zhen Guo, Bing College of Computer Science Sichuan University Chengdu China

ISBN: (纸本)9781510655942

As deep neural network models become more and more complex and the advent of massive amounts of data, distributed computing is needed to support the training process of the network model. Blockchain is a new distributed infrastructure and computing paradigm. Training the deep neural network model by converging the computing power of each node through blockchain technology will make the underlying consensus algorithm of the blockchain more practical and at the same time solve the problem of insufficient deep learning computing power. It studies the combination of blockchain and training of deep neural network model. It designs the consensus mechanism of the blockchain, uses the distributed training process of the deep neural network model as the proof of work of the nodes, encourages each node to contribute computing power through the incentive mechanism, and records the important data generated during the computing process in the blockchain. © 2022 SPIE.

关键词： Deep neural networks

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Continuous time PDE prediction with different parameters and effective grid input domain 3

Continuous time PDE prediction with different parameters and...

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3rd International Conference on Applied Mathematics, Modelling, and Intelligent computing, CAMMIC 2023

作者： Wang, Ziquan 9 Nansan Street Shahe Higher Education Park Changping Beijing102206 China

ISBN: (数字)9781510667617

ISBN: (纸本)9781510667600

Due to issues such as uneven spatial distribution of data sensors and data vulnerability, dynamic systems such as disease transmission and weather prediction that can be described by PDE often generate uneven spatiotemporal data. However, due to the limitations of module assumptions or convolutional calculations, existing data-driven deep learning models are often not suitable for spatiotemporal data that is not standardized in the spatiotemporal domain, and specific models are difficult to generalize to new dynamic systems. In this paper, graph neural network and method of line method are used to model the uneven spatio-temporal data modeling generated by dynamic systems, and a prediction model that allows efficient spatio-temporal input domain is proposed. It uses a differential graph network of approximate derivatives to learn the differential information in dynamic system data, combined with the dynamic system parameter information extracted from the parameter coding network, and finally realizes accurate prediction of dynamic systems through network approximate equations. The model achieves end-to-end system prediction, in which the parameter encoding network learns dynamic system parameters and other information, and is supplemented by a meta learning mechanism to improve the model's generalization ability on different PDE prediction tasks. The experimental results on the nonlinear reaction diffusion equation and heat equation show that the model can adapt to the data with spatiotemporal noise, and shows a very competitive prediction accuracy. © 2023 SPIE.

关键词： Graph neural networks

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Artificial instinct

Artificial instinct

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2024 International Conference on Algorithms, High Performance computing, and Artificial Intelligence, AHPCAI 2024

作者： Li, Yahui Wang, Jilong Institute for Network Sciences and Cyberspace Tsinghua University Beijing China

ISBN: (纸本)9781510685840

Artificial Intelligence (AI) has made remarkable advancements, surpassing human capabilities in various domains. This paper delves into cutting-edge AI technologies, analyzing significant models and techniques. However, current AI models have not fully captured the essence of human instincts, which we believe to be the core differentiator between humans and machines. We further explored simple human instinctive behaviors using a cellular automata (CA) model. While CA successfully emulated basic instincts, it fell short in replicating complex instinctive behaviors. We propose that future collaborations between computer science researchers and neuroscientists could offer the potential for gaining deeper insights into the underlying mechanisms of human instincts, developing advanced "artificial instinct" technology and producing the next generation of revolutionary machine systems. © 2024 SPIE.

关键词： Artificial intelligence Machine learning Systems modeling neural networks Data modeling Performance modeling Education and training Deep learning Transformers Speaker recognition

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Bit-level Sparsity Deep Learning Accelerator embedded with the binary neural network: A Survey

Bit-level Sparsity Deep Learning Accelerator embedded with t...

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2022 International Conference on Computer Graphics, Artificial Intelligence, and Data Processing, ICCAID 2022

作者： Zhang, Yechengnuo MSc Microelectronic System Design University of Southampton Southampton HampshireSO17 1BJ United Kingdom

ISBN: (纸本)9781510663350

neural network accelerators provide deep learning algorithms with a faster and more energy-efficient way of computing. Among those hardware accelerators, the bit-level sparsity accelerator seeks the "0" bits in weights and activations to skip the ineffectual computation. Currently, bit-level sparsity accelerators have well explored the possible sparsity in various CNN models with different parameter precision. However, the large number of parameters in CNN occupies many memory storages and troubles designers to exploit the sparsity. Therefore, this paper introduces the Binary neural network (BNN) to shorten the computation time and energy consumption with acceptable losses of accuracy. In this paper, the research approach is based on both bit-level sparsity accelerators and BNN algorithms. Some current studies on bit-level sparsity accelerators are shown first, then BNN is introduced. The result provides a framework of bit-level sparsity deep learning accelerator embedded with the binary neural network. © 2023 SPIE.

关键词： Energy utilization

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