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3D Vision Transformer for Postoperative Recurrence Risk Prediction of Liver Cancer

3D Vision Transformer for Postoperative Recurrence Risk Pred...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Li, Fan Zhou, Xueying Gao, Xizhan Zhao, Hui Niu, Sijie School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789811669620

Hepatocellular carcinoma (HCC) is a kind of malignant tumor with a high fatality rate, and it has a serious impact on the patient’s normal life. Although the diagnostic scheme for liver cancer has been gradually improved in recent years, the prognosis of patients with liver cancer is still not very ideal. Due to the high recurrence rate and poor prognosis after surgery, intrahepatic metastasis plays an important role in the survival cycle of patients with liver cancer, which is also a significant guiding factor in the selection of preoperative treatment and the planning of postoperative follow-up. In this paper, a classification method based on 3D Vision Transformer is proposed to determine whether intrahepatic metastasis occurs after the operation. Specifically, in this study, 161 patients who were diagnosed as HCC by puncture pathology or clinical diagnosis and received Transcatheter Arterial Chemoembolization (TACE) treatment were selected as the study subjects, and the patients were divided into non-metastasis group (79 cases) and intrahepatic metastasis group (82 cases). By comparing 3D Vision Transformer with the 3D forms of multiple deep learning classification models, the predictive value of 3D Vision Transformer in predicting intrahepatic metastasis after transarterial chemoembolization in HCC patients was verified. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Pathology

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Federated Meta Continual Learning for Efficient and Autonomous Edge Inference 24th

Federated Meta Continual Learning for Efficient and Autono...

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24th International Conference on Algorithms and Architectures for Parallel Processing, ICA3PP 2024

作者： Li, Bingze Ho, Stella Qu, Youyang Xu, Chenhao Luan, Tom H. Gao, Longxiang Jinan China Shandong Provincial Key Laboratory of Computing Power Internet and Service Computing Shandong Fundamental Research Center for Computer Science Jinan China Department of Biomedical Engineering The University of Melbourne VIC Melbourne Australia School of Information Technology Deakin University VIC Geelong Australia School of Cyber Engineering Xidian University Xi’an China

ISBN: (纸本)9789819615476

With an increasing volume of data being collected and processed on edge, Federated Learning (FL) has become a dominant approach to train shared global models on distributed data held by diverse edge clients without exposing privacy. However, with expectations of high-quality user experience, instantaneous intelligent decisions must be made at the network edge to meet performance, security, and latency requirements. Intuitively, this challenge becomes more daunting as edge devices are constrained by limited computing and storage resources. Furthermore, edge clients need to continuously acquire, update, accumulate, and apply knowledge in domains requiring the processing of sensitive data and long-term model training. It has been proven that edge clients often forget previous tasks knowledge after learning new tasks, resulting in catastrophic forgetting. To mitigate this, we propose a Federated Meta Continual Learning (FMCL) approach. It requires edge clients not only to learn new tasks quickly but also to memorize rapidly. Specifically, we adopted a MAML (Model-Agnostic Meta-Learning) framework with memory replay, setting up buffers at edge clients to consolidate old tasks. In such a way, the model learns optimal initialization to quickly adapt to all seen tasks while preventing catastrophic forgetting. Extensive evaluation results indicate that FMCL performs well in alleviating catastrophic forgetting and achieves a good balance between learning new tasks and remembering existing tasks. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2025.

关键词： Federated learning

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A Survey of FPGA-Based Deep Learning Acceleration Research

A Survey of FPGA-Based Deep Learning Acceleration Research

引用

International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Lv, Ziyi Zhang, Jing School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789811669620

In a range of fields such as emotion detection, medical image processing and speech recognition, deep learning has recently achieved good results. With the pursuit of more precise results, many scholars try to add more new type network layers to increase the size of the neural network. However, this will lead to deeper and more intricate network models, and training and evaluating models requires intensive CPU calculations and tremendous computing resources which cannot be achieved by general purpose processors. Nowadays, some hardware accelerators such as Field Programmable Gate Array (FPGA) have been employed to accelerate the neural network, and FPGA with reconfigurability and low power consumption are currently applied to improve throughput of deep learning networks at a reasonable price. In this paper, the typical technologies and methods of accelerating deep learning network on FPGA in recent years are reviewed and analyzed with their advantages and disadvantages, and feasible research suggestions for the next research direction are given. It is expected that it will have a certain reference value for researchers in the field of deep learning acceleration and hardware optimization. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Deep learning

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Attention-Aware U-Net network for Segmentation of Retinopathy Region

Attention-Aware U-Net Network for Segmentation of Retinopath...

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International Conference on Image, Vision and Intelligent Systems, ICIVIS 2021

作者： Kong, Wenyang Li, Fan Xing, Ruiwen Gao, Xizhan Zhao, Hui Su, Jie Niu, Sijie School of Information Science and Engineering University of Jinan Jinan250022 China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9789811669620

The important function of the computer-aided analysis platform for OCT (Optical Coherence Tomography) retinal images is image segmentation and the accuracy of the segmentation results affects the diagnosis of the disease area. In this paper, we improve the U-Net network by introducing attention mechanism and residual learning for segmenting retinal image lesions. Based on the original U-Net architecture, we embed the Squeeze-and-excitation module into the residual learning branch of the residual structure to replace the backbone network of U-Net. As a result, our improved method not only retains the advantages of fast convergence of network and Solve the problem of the weakening of the neural network's ability to extract features in the deep level, but also considers the correlation between attention learning channels, enhances useful features, and suppresses noise features. The data set used for training and testing of the method in this paper contains 2944 images from 23 patients diagnosed with ocular CSC (Central Serous Chorioretinopathy). We performed 4 sets of cross-validation on this dataset. According to experimental data, the method proposed in this paper can get better segmentation results. Compared with seven methods, our improved method can achieve competitive segmentation results, which is helpful for clinical disease diagnosis. © 2022, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Optical tomography

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Multiple Random Feature Superpixel-Based Fuzzy Clustering for Image Segmentation 9

Multiple Random Feature Superpixel-Based Fuzzy Clustering fo...

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9th International Conference on information, Cybernetics, and Computational Social Systems, ICCSS 2023

作者： Zhang, Chuanbin Chen, Long Tian, Yifei Shi, Zhaoyin Wang, Yingxu University of Macau Department of Computer and Information Science China School of Computer Science Nanjing University of Posts and Telecommunications Nanjing China Shandong Provincial Key Laboratory of Network-Based Intelligent Computing University of Jinan Jinan China

ISBN: (纸本)9798350342239

Random feature maps attempt to approximate the kernel method with low computational complexity, and they are efficient and effective algorithms for dealing with the non-linear structure of data. Nevertheless, the existing random feature-based methods are sensitive to hyper-parameters, and their performances are affected by the randomness in feature maps. To improve the robustness of random feature maps and further speed up kernel-based image segmentation algorithms, a multiple random feature superpixel-based fuzzy clustering is proposed in this paper. First, we generate a group of random features from the original image via a novel multiple random feature scheme. Then, we use the superpixel segmentation to reduce the scale of multiple random features. The final segmentation results are obtained by performing the multi-view fuzzy clustering on the multiple random feature superpixels. Experiments on two shared benchmark data sets verify the superiority of our approach. © 2023 IEEE.

关键词： Image segmentation

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Large Scale Model Enabled Semantic Communications Based on Robust Knowledge Distillation

Large Scale Model Enabled Semantic Communications Based on R...

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2024 IEEE Global Communications Conference, GLOBECOM 2024

作者： Ding, Kuiyuan Liu, Fangfang Yang, Yang Chen, Mingzhe Guo, Caili Beijing University of Posts and Telecommunications Beijing Key Laboratory of Network System Architecture and Convergence School of Information and Communication Engineering Beijing100876 China University of Miami Department of Electrical and Computer Engineering The Institute for Data Science and Computing Coral GablesFL33146 United States Beijing University of Posts and Telecommunications Beijing Laboratory of Advanced Information Networks School of Information and Communication Engineering Beijing100876 China

ISBN: (纸本)9798350351255

Large scale artificial intelligence (AI) models possess excellent capabilities in semantic representation and understanding, making them particularly well-suited for semantic encoding and decoding. However, the substantial scale of these AI models imposes unacceptable computational resources and communication delays. To address this issue, we propose a semantic communication scheme based on robust knowledge distillation (RKD-SC) for large scale model enabled semantic communications. In the considered system, a transmitter extracts the features of the source image for robust transmission and accurate image classification at the receiver. To effectively utilize the superior capability of large scale model while make the cost affordable, we first transfer knowledge from a large scale model to a smaller scale model to serve as the semantic encoder. Then, to enhance the robustness of the system against channel noise, we propose a channel-aware autoencoder (CAA) based on the Transformer architecture. Experimental results show that the encoder of proposed RKD-SC system can achieve over 93.3% of the performance of a large scale model while compressing 96.67% number of parameters. Code: https://***/echojayne/RKD-SC. © 2024 IEEE.

关键词： Channel coding

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NeuVSA: A Unified and Efficient Accelerator for Neural Vector Search 31

NeuVSA: A Unified and Efficient Accelerator for Neural Vecto...

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31st IEEE International Symposium on High Performance Computer Architecture, HPCA 2025

作者： Yuan, Ziming Dai, Lei Li, Wen Zhang, Jie Liang, Shengwen Wang, Ying Liu, Cheng Li, Huawei Li, Xiaowei Guo, Jiafeng Wang, Peng Chen, Renhai Zhang, Gong Shanxi University School of Computer and Information Technology China Peking University School of Computer Science China Institute of Computing Technology CAS State Key Lab of Processors China University of Chinese Academy of Sciences China Zhongguancun National Laboratory Beijing China Institute of Computing Technology CAS Key Lab of Network Data Science and Technology China Huawei Technologies Co. Ltd. China

ISBN: (纸本)9798331506476

Neural Vector Search (NVS) has exhibited superior search quality over traditional key-based strategies for information retrieval tasks. An effective NVS architecture requires high recall, low latency, and high throughput to enhance user experience and cost-efficiency. However, implementing NVS on existing neural network accelerators and vector search accelerators is sub-optimal due to the separation between the embedding stage and vector search stage at both algorithm and architecture levels. Fortunately, we unveil that Product Quantization (PQ) opens up an opportunity to break separation. However, existing PQ algorithms and accelerators still focus on either the embedding stage or the vector search stage, rather than both simultaneously. Simply combining existing solutions still follows the beaten track of separation and suffers from insufficient parallelization, frequent data access conflicts, and the absence of scheduling, thus failing to reach optimal recall, latency, and throughput. To this end, we propose a unified and efficient NVS accelerator dubbed NeuVSA based on algorithm and architecture co-design philosophy. Specifically, on the algorithm level, we propose a learned PQ-based unified NVS algorithm that consolidates two separate stages into the same computing and memory access paradigm. It integrates an end-to-end joint training strategy to learn the optimal codebook and index for enhanced recall and reduced PQ complexity, thus achieving smoother acceleration. On the architecture level, we customize a homogeneous NVS accelerator based on the unified NVS algorithm. Each sub-accelerator is optimized to exploit all parallelism exposed by unified NVS, incorporating a structured index assignment strategy and an elastic on-chip buffer to alleviate buffer conflicts for reduced latency. All sub-accelerators are coordinated using a hardware-aware scheduling strategy for boosted throughput. Experimental results show that the joint training strategy improves recall

关键词： network embeddings

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Non-Destructive Judgment of Watermelon Ripeness Based on Multi-Feature Fusion of Image

Non-Destructive Judgment of Watermelon Ripeness Based on Mul...

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International Conference on Fuzzy Systems and Knowledge Discovery (FSKD)

作者： Hongbao Shi Jun Liu Yimei Huang Jinping Li Shandong Provincial Key Laboratory of Network Based Intelligent Computing School of Information Science and Engineering University of Jinan Jinan China

ISBN: (数字)9798350356328

ISBN: (纸本)9798350356335

The nondestructive judgment of watermelon ripeness is a key issue in everyday life. Whether watermelons are ripe or not affects the interests of watermelon farmers and consumers. So far, acoustic, electrical, machine vision and other methods have been applied to the nondestructive judgment of agricultural product ripeness, but the equipment used in the lab, such as spectrometers and laser Doppler vibrometers, is expensive, difficult to carry, and has limited practical value in daily life. Based on practical experience, the ripeness of watermelons can be judged by observing specific physical characteristics, such as the rind's glossiness, texture, fuzz on the fruit stalk and navel. In this study, images of the watermelon's texture, fuzz and navel were captured using a smartphone. We utilized ResN et with an embedded Coordinate Attention mechanism and Swin-Transformer networks to independently analyze the ripeness of each feature. Then we combined the output prediction scores using post-fusion technology, with the highest weighted prediction score judging the final ripeness assessment. Our method achieved a 90.2% accuracy rate on the test set. The experiments demonstrate that a deep learning model utilizing multi-feature fusion of image data can reliably and accurately identify watermelon ripeness, even under varying image conditions.

关键词： Deep learning Laser theory Accuracy Vibrometers Machine vision Morphology Knowledge discovery Mobile handsets Reliability Guidelines

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A dwell point detection algorithm for smart pen 2

A dwell point detection algorithm for smart pen

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2nd International Conference on Computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Kong, DeHui University of Jinan School of Information Science and Engineering Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9781665492508

This paper presents a dwell point detection algorithm for smart pen. The main innovations of the method are: (a) The tracking of a smart pen is achieved by a common monocular camera. (b) A method of vertex adsorption is proposed. (c) The definition of a dwell point is introduced, and the auxiliary line intent is calculated by detecting the dwell point. Compared to the collision body detection method in Unity3D, the method in this paper improves the accuracy of doing auxiliary line intent and the smoothness of user operation. The effectiveness of the method is demonstrated by the use of the method in a smart pen interacting with a Unity3D-based three-dimensional geometry teaching system. © 2022 IEEE.

关键词： Virtual reality

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Multimodal intention understanding method based on NHMM 2

Multimodal intention understanding method based on NHMM

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2nd International Conference on Computer Graphics, Image and Virtualization, ICCGIV 2022

作者： Liu, Hao University of Jinan School of Information Science and Engineering Jinan250022 China Shandong Provincial Key Laboratory of Network Based Intelligent Computing Jinan250022 China

ISBN: (纸本)9781665492508

This paper proposes a multimodal intention understanding method based on NHMM. Due to the declining expression ability of the elderly, single-mode input during human-computer interaction may reduce the accuracy of robot intention understanding and cannot meet the requirements of helping the elderly. Therefore, in the aspect of intention understanding, this paper uses multi-modal input of voice, gesture and posture to obtain the user's multi-dimensional information as much as possible. Each modal information is processed to obtain three intention sets, and then fused to obtain the fused intention set, By combining the improvement of the core formula of hidden Markov model (HMM) with Bayes, the intention with the highest probability in the fusion intention set is calculated as the result of intention understanding. The results show that the accuracy of this method is 97.7% in the state of user natural interaction, and compared with the other four intention understanding methods, this method is more effective for intention extraction under fuzzy input. © 2022 IEEE.

关键词： Human computer interaction

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