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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Kun Tang Lihui Wang Menglong Yang Jingwen Xu Xinyu Cheng Jian Zhang Yuemin Zhu Hongjiang Wei Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province Engineering Research Center of Text Computing & Cognitive Intelligence Ministry of Education State Key Laboratory of Public Big Data College of Computer Science and Technology Guiyang China INSA Lyon CNRS Inserm Univ Lyon Lyon France School of Biomedical Engineering Shanghai Jiao Tong University Shanghai China

ISBN: (数字)9798350386226

ISBN: (纸本)9798350386233

Considering the tumor aggressive nature and the significant changes in anatomical structure, aligning the preoperative and follow up scans of glioma patients remains a challenge due to the presence of regions with absent correspondence. To address this challenge, this work proposed a novel bidirectional unsupervised deformable image registration framework for image pairs with missing correspondence based on an auxiliary-image-aided intensity-consistency constraint (ICC) strategy. Specifically, for any fixed and moving image pairs, we introduced an auxiliary image and warped it directly to fixed/moving image or warped it twice through a transition of moving/fixed image. By comparing the difference between these warped images, the weighting maps to identify and exclude regions with absent correspondence between fixed and moving image pairs can be generated. To verify the effectiveness of the proposed framework, we combined it with several deep learning-based registration models and tested it on BraTS-Reg challenge dataset, the results demonstrated that the proposed ICC strategy can improve the registration performance for all the models, with the improvement of average target registration error (TRE) and success rate (SR) being up to 44.9% and 66.7%, respectively. Comparing against the best existing forward-backward consistency strategy for dealing with missing correspondence registration, our auxiliary-image-aided ICC strategy can also decrease average TRE by 2.9%, demonstrating the superiority of the proposed framework. The present work is not limited to the glioma images, it can be used to address the registration problems for any image pairs with absent correspondence or inconsistent intensity.

关键词： image registration Pathology Deformation Magnetic resonance imaging Anatomical structure Bioinformatics Tumors Biomedical imaging

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PVGAN: A Pathological Voice Generation Model Incorporating a Progressive Nesting Strategy

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Journal of Voice 2023年

作者： Pan, Xiaoying Feng, Tong Zhang, Nijuan Shaanxi Key Laboratory of Network Data Analysis and Intelligent Processing Xi'an University of Posts and Telecommunications Xi'an 710121 China School of Computer Science & Technology Xi'an University of Posts and Telecommunications Xi'an 710121 China

The voice generation task is to solve the problem of limited samples in the voice dataset using computer technology. By increasing the number of samples, the accuracy of voice disorder diagnosis can be improved, which has a wide range of application value in medical diagnosis and other fields. At present, there are insufficient models for detailed features such as pitch, timbre, and different frequency components in pathological voice data. Therefore, this paper proposes a PVGAN network for learning different frequency information of audio to generate pathological voice data. The proposed network captures the multi-scale features and different periodic patterns of audio signals by designing multiscale perceptual residual blocks and periodic discriminators. At the same time, a progressive nesting strategy was proposed to combine the generator and the discriminator to improve the learning ability of different resolution information. In addition, a latent mapping network is designed to fuse the latent vector with the condition information to generate sound features related to specific diseases or pathological states. The loss function is optimized to further improve the model performance. On the Saarbruecken Voice database(SVD), the average values of each index of the data generated after training with different pathological types as conditional information are similar to the original data. Finally, the generated data were used to expand the SVD dataset, and the accuracy of the two classification experiments was improved to a certain extent. © 2023 The Voice Foundation

关键词： Deep learning Generative adversarial networks Voice analysis Voice generation

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Tumor State-Space Network for High and Low Grade Glioma Classification

Tumor State-Space Network for High and Low Grade Glioma Clas...

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International Conference on Signal processing Proceedings (ICSP)

作者： Qijian Chen Lihui Wang Zeyu Deng Li Wang Chen Ye YueMin Zhu Engineering Research Center of Text Computing Ministry of Education Key Laboratory of Intelligent Medical Image Analysis and Precise Diagnosis of Guizhou Province State Key Laboratory of Public Big Data College of Computer Science and Technology Guizhou University Guiyang China INSA Lyon CNRS Inserm IRP Metislab CREATIS UMR5220 U1206 University Lyon Lyon France

ISBN: (数字)9798350387384

ISBN: (纸本)9798350387391

Accurately predicting the grade of gliomas is crucial for choosing right treatment plans. While current methods using radiomics and deep learning can predict glioma grades effectively using magnetic resonance imaging (MRI), they overlook dynamic changes in the tumor and its neighboring areas. To address this issue, we propose a tumor state-space network (TSSNet), which fully exploits changes in the tumor region and its surroundings through dynamical updating strategy. The experimental results demonstrated that TSSNet achieves 90.32% accuracy and 93.55% area under the curve (AUC) for the prediction of high-grade and low-grade gliomas, respectively, which are 2.4% and 2.5% higher than the best state-of-the-art deep learning models. Moreover, the proposed state-space module can effectively enhance prediction performance, which is advantageous for personalized glioma treatment.

关键词： Deep learning Accuracy Magnetic resonance imaging Signal processing Predictive models Tumors Radiomics

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Enhancing Event Tagger with Automatic Speech Recognizer for Audio Multi-task Scenarios by Distillation with Pre-Trained Large Models

Enhancing Event Tagger with Automatic Speech Recognizer for ...

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IEEE International Conference on Systems, Man and Cybernetics

作者： Jianfeng Cheng Ye Liu Jian Yin Liangdao Wang Yan Pan Artificial Intelligence Department Lizhi Inc. Beijing & Guangzhou China School of Computer Science and Engineering Sun Yat-sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Artificial Intelligence Sun Yat-sen University Zhuhai China

ISBN: (数字)9781665410205

ISBN: (纸本)9781665410212

With the continuous expansion of robotics and digital humans in practical applications, the demand for the auditory system is becoming deeper, usually requiring more efficient speech recognition framework capabilities to handle multiple tasks and use fewer resources. In previous audio processing frameworks, each type of audio processing task typically requires constructing a standalone deep network model for training, which results in more training data and higher training time when constructing models for multi-task audio scenarios simultaneously. The recent improvement of audio models based on transformers have brought about methods that can handle multiple audio tasks concurrently. However, recent related methods still require retraining multi-task targets with an amount of data, and achieve the general effect after training for multi-task scenarios than the simple combination of standalone methods processed separately. In order to better build a model that can handle multiple audio tasks, we propose a novel framework of distillation through pre-trained large models for enhancing event tagger with automatic speech recognizer. Through multiple rounds of experiments on several audio datasets, it has been verified that the proposed framework can achieve better results than the baseline for multitasking, and comparative results with less parameters compared to the baselines for single-task scenarios.

关键词： Training Adaptation models Analytical models Training data Speech recognition Speech enhancement Multitasking Transformers data models Robots

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A review of big data technology and its application in cancer care

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Computers in Biology and Medicine 2024年 176卷 108577-108577页

作者： Xiao, Tianyun Kong, Shanshan Zhang, Zichen Hua, Dianbo Liu, Fengchun Hebei Key Laboratory of Data Science and Application North China University of Science and Technology Hebei Tangshan063210 China The Key Laboratory of Engineering Computing in Tangshan City North China University of Science and Technology Hebei Tangshan063210 China College of Science North China University of Science and Technology Hebei Tangshan063210 China Hebei Engineering Research Center for the Intelligentization of Iron Ore Optimization and Ironmaking Raw Materials Preparation Processes North China University of Science and Technology Hebei Tangshan China Tangshan Intelligent Industry and Image Processing Technology Innovation Center North China University of Science and Technology Hebei Tangshan China Beijing Sitairui Cancer Data Analysis Joint Laboratory Beijing101149 China

The development of modern medical devices and information technology has led to a rapid growth in the amount of data available for health protection information, with the concept of medical big data emerging globally, along with significant advances in cancer care relying on data-driven approaches. However, outstanding issues such as fragmented data governance, low-quality data specification, and data lock-in still make sharing challenging. Big data technology provides solutions for managing massive heterogeneous data while combining artificial intelligence (AI) techniques such as machine learning (ML) and deep learning (DL) to better mine the intrinsic connections between data. This paper surveys and organizes recent articles on big data technology and its applications in cancer, dividing them into three different types to outline their primary content and summarize their critical role in assisting cancer care. It then examines the latest research directions in big data technology in cancer and evaluates the current state of development of each type of application. Finally, current challenges and opportunities are discussed, and recommendations are made for the further integration of big data technology into the medical industry in the future. © 2024 Elsevier Ltd

关键词： Big data

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Global image correlation filter with H-D fusion mechanism for visual tracking 9

Global image correlation filter with H-D fusion mechanism fo...

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9th International Conference on Computing and Pattern Recognition, ICCPR 2020

作者： Yi, Yang Chen, Jiaqian Liang, Da'An Tian, Guixiong School of Data and Computer Science Sun Yat-sen University School of Information Science !!!Xinhua College of Sun Yat-sen University @@@Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China School of Data and Computer Science Sun Yat-sen University Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9781450387835

The discriminative correlation filter has remarkable robustness under various circumstances in the visual tracking of videos. Nevertheless, there are some challenges for the further improvement of the tracking performance, including confusion between the tracked target, complex background, and underfitting because of a lack of training samples. The main contribution of the study is developing a new framework which combines with global image sampling enable to significantly augment the negative samples without the need of corruptting those positive samples. Besides we propose a fusion mechanism by combining two image patch representations in accordance with their confidence scores. Obtained results show that the proposed method is an effective scheme in leveraging the complementary properties of deep and hand-crafted features. Based on the conducted experiments on several benchmarks such as OTB2013, TB-50, and TB-100 datasets, it is concluded that the proposed approaches can achieve better result compared to the state-ofthe-art methods. © 2020 ACM.

关键词： Computer vision

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Research on Visualization Algorithm of Association Rules Based on Concept Lattice

Research on Visualization Algorithm of Association Rules Bas...

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2020 International Conference on Cyberspace Innovation of Advanced Technologies, CIAT 2020

作者： Shen, Xiajiong Bao, Lijie Zhang, Lei Institute of Data and Knowledge Engineering Henan University Kaifeng China Henan Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China

ISBN: (纸本)9781450387828

The traditional association rule representation method has no way to show the essential relationship between concepts and lacks understanding of the concept level. Concept lattice is a kind of data structure which can show the relationship between concepts, concept lattice also has unique advantages in the visualization and discovery of potential knowledge of association rules. In this paper, a visualization algorithm of association rules based on concept lattice is proposed. The algorithm visually displays the association rules extracted from concept lattice. Finally, this article combines the chronic disease record data of a certain hospital to analyze and visualize the association rules. The implementation results show that the visualization method has good effects in knowledge analysis and data display. © 2020 ACM.

关键词： Visualization

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Split and Attentive-Aggregated Learnable Shift Module for Video Action Recognition 20

Split and Attentive-Aggregated Learnable Shift Module for Vi...

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3rd International Conference on Machine Learning and Machine Intelligence, MLMI 2020

作者： Wu, Xiao Ji, Qingge School of Data and Computer Science Sun Yat-Sen University Guangzhou China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China

ISBN: (纸本)9781450388344

Existing approaches for video action recognition using convolutional neural network (CNN) usually suffer from the trade-off between accuracy and complexity. On the one hand, the 2D CNNs have difficulty in modeling the long-term temporal dependencies though they are computationally cheap. On the other hand, 3D CNNs have the ability to perceive temporal cues however lead to a high computational cost. In this paper, we propose a generic building block named Split and Attentive-aggregated Learnable Shift Module (SALSM) which has capacity of modeling spatiotemporal representations while maintain the complexity of the 2D CNN. Specifically, we split the input tensor into multiple groups, and conduct adaptive shift operations by applying the learnable shift kernels for different channels of each group along time dimension, so that the spatiotemporal information from neighboring frames can be mingled with 2D convolutions. The output feature maps of each group are integrated together with attention mechanism. With SALSM plugged in, the 2D CNN is enhanced to handle temporal information and become a highly efficient spatiotemporal feature extractor with little parameters and computational cost. We conduct ablation experiments to verify the effectiveness of our method, and our proposed SALSM achieves competitive or even better results over the state-of-the-art methods on several benchmark datasets. © 2020 ACM.

关键词： Convolutional neural networks

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3D Virtual Sand Table Display System of Industrial Park Based on Augmented Reality

3D Virtual Sand Table Display System of Industrial Park Base...

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2021 IEEE International Conference on Industrial Application of Artificial Intelligence, IAAI 2021

作者： Lu, Hongyan He, Jie Cen, Caichun He, Jie Wuzhou University School of Big Data and Software Engineering Wuzhou China Wuzhou University School of Electronic and Information Engineering Wuzhou China Key Laboratory of Image Processing and Intelligent Information System at Guangxi University Wuzhou University Wuzhou China

ISBN: (纸本)9781665495066

To improve the interactivity of 3D virtual sand table displays, an industrial park 3D virtual sand table display system based on augmented reality is designed. The system consists of multiple modules. The design of the animation browsing module is based on the roaming walking model environment. The walking and roaming module can enable participants to experience the real visual and tactile virtual space in the process of input browsing. In the surrounding environment and traffic condition module, the surrounding environment and traffic condition display are created by means of static pictures and picture buttons. The VRML scene space module is responsible for creating objects such as VRML viewpoints. Scenario module transformation completes event transmission through routing. The virtual sand table VRML optimization modeling module is responsible for constructing the virtual sand table VRML model. The function of the multiperson collaboration module is to provide collaboration capability for plotting and discussion functions. In the multiuser detection module, yolov2 is used for multiuser detection. The augmented reality hardware module is mainly composed of computers and hololens. Through the combination of hardware and software, a three-dimensional virtual sand table display of an industrial park is realized. The test results show that the performance of the designed system is good and can meet the design requirements. © 2021 IEEE.

关键词： Augmented reality

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Clip-Ranker: From Pointwise to Listwise Ranker for Multimodal Retrieval

SSRN

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SSRN 2022年

作者： Xu, Mengying Lai, Hanjiang Yin, Jian Luo, Wei SUN YAT-SEN UNIVERSITY No. 135 Xingang Xi Road Guangzhou Guangzhou510275 China Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou510006 China

Fine-tuning large-scale pre-trained vision-language models have shown significant progress on various downstream multimodal tasks. As the pretrained model size grows, it becomes prohibitive to fine-tune all parameters of the model in low resource scenarios. Recently, the CLIP-based approaches have achieved impressive results on the downstream multimodal retrieval task, which are the parameter-efficient methods. However, current CLIP-based adapters only consider one image-text pair, and lack the use of special ranking information of multiple samples, which may result in performance descent. In this paper, we propose a ranking-aware adapter for multimodal retrieval, called CLIP-Ranker, which is to achieve a better adapter model that exploits the ranking information. First, we learn a pointwise ranker, which fine-tunes the additional layers to align the visual and textual representation learning. Then, a listwise ranker is proposed, where we rerank the top results from the pointwise ranker and the lists of top samples are used as instances in learning by Transformer to get the final ranking-aware scores. We conduct extensive experiments on Flickr30K, MS coco, and IAPR three benchmark datasets. The results show that the proposed CLIP-ranker retrieval method can achieve better performance. © 2022, The Authors. All rights reserved.

关键词： Machine learning

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