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An artificially intelligent magnetic resonance spectroscopy quantification method: Comparison between QNet and LCModel on the cloud computing platform CloudBrain-MRS

arXiv

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arXiv 2025年

作者： Lin, Meijin Guo, Lin Chen, Dicheng Chen, Jianshu Tu, Zhangren Huang, Xu Wang, Jianhua Qi, Ji Long, Yuan Huang, Zhiguo Guo, Di Qu, Xiaobo Han, Haiwei College of Ocean & Earth Sciences Xiamen University Xiamen361102 China Fujian Provincial Key Laboratory of Plasma and Magnetic Resonance Xiamen University Xiamen361102 China National Integrated Circuit Industry Education Integration Innovation Platform Xiamen University Xiamen361100 China Department of Electronic Science Xiamen University Xiamen361102 China Department of Radiology the First Affiliated Hospital of Xiamen University School of Medicine Xiamen University Xiamen361100 China Software Technology Company Limited Suzhou215163 China School of Computer and Information Engineering Fujian Engineering Research Center for Medical Data Mining and Application Xiamen University of Technology Xiamen361024 China

Objctives: This work aimed to statistically compare the metabolite quantification of human brain magnetic resonance spectroscopy (MRS) between the deep learning method QNet and the classical method LCModel through an easy-to-use intelligent cloud computing platform CloudBrain-MRS. Materials and Methods: In this retrospective study, two 3 T MRI scanners Philips Ingenia and Achieva collected 61 and 46 in vivo 1H magnetic resonance (MR) spectra of healthy participants, respectively, from the brain region of pregenual anterior cingulate cortex from September to October 2021. The analyses of Bland-Altman, Pearson correlation and reasonability were performed to assess the degree of agreement, linear correlation and reasonability between the two quantification methods. Results: Fifteen healthy volunteers (12 females and 3 males, age range: 21-35 years, mean age ± standard deviation = 27.4 ± 3.9 years) were recruited. The analyses of Bland-Altman, Pearson correlation and reasonability showed high to good consistency and very strong to moderate correlation between the two methods for quantification of total N-acetylaspartate (tNAA), total choline (tCho), and inositol (Ins) (relative half interval of limits of agreement = 3.04%, 9.3%, and 18.5%, respectively;Pearson correlation coefficient r = 0.775, 0.927, and 0.469, respectively). In addition, quantification results of QNet are more likely to be closer to the previous reported average values than those of LCModel. Conclusion: There were high or good degrees of consistency between the quantification results of QNet and LCModel for tNAA, tCho, and Ins, and QNet generally has more reasonable quantification than LCModel. © 2025, CC BY.

关键词： Metabolites

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CKD-TransBTS: Clinical Knowledge-Driven Hybrid Transformer with Modality-Correlated Cross-Attention for Brain Tumor Segmentation

arXiv

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arXiv 2022年

作者： Lin, Jianwei Lin, Jiatai Lu, Cheng Chen, Hao Lin, Huan Zhao, Bingchao Shi, Zhenwei Qiu, Bingjiang Pan, Xipeng Xu, Zeyan Huang, Biao Liang, Changhong Han, Guoqiang Liu, Zaiyi Han, Chu The School of Computer Science and Engineering South China University of Technology Guangzhou510006 China The Department of Radiology Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangdong Provincial People’s Hospital Guangdong Academy of Medical Sciences Guangzhou510080 China The Department of Computer Science and Engineering The Hong Kong University of Science and Technology Clear Water Bay Hong Kong

Brain tumor segmentation (BTS) in magnetic resonance image (MRI) is crucial for brain tumor diagnosis, cancer management and research purposes. With the great success of the ten-year BraTS challenges as well as the advances of CNN and Transformer algorithms, a lot of outstanding BTS models have been proposed to tackle the difficulties of BTS in different technical aspects. However, existing studies hardly consider how to fuse the multi-modality images in a reasonable manner. In this paper, we leverage the clinical knowledge of how radiologists diagnose brain tumors from multiple MRI modalities and propose a clinical knowledge-driven brain tumor segmentation model, called CKD-TransBTS. Instead of directly concatenating all the modalities, we re-organize the input modalities by separating them into two groups according to the imaging principle of MRI. A dual-branch hybrid encoder with the proposed modality-correlated cross-attention block (MCCA) is designed to extract the multi-modality image features. The proposed model inherits the strengths from both Transformer and CNN with the local feature representation ability for precise lesion boundaries and long-range feature extraction for 3D volumetric images. To bridge the gap between Transformer and CNN features, we propose a Trans&CNN Feature Calibration block (TCFC) in the decoder. We compare the proposed model with five CNN-based models and six transformer-based models on the BraTS 2021 challenge dataset. Extensive experiments demonstrate that the proposed model achieves state-of-the-art brain tumor segmentation performance compared with all the competitors. © 2022, CC BY.

关键词： Brain

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Semi-Supervised and Self-Supervised Collaborative Learning for Prostate 3D MR Image Segmentation

arXiv

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arXiv 2022年

作者： Osman, Yousuf Babiker M. Li, Cheng Huang, Weijian Elsayed, Nazik Xue, Zhenzhen Zheng, Hairong Wang, Shanshan Paul C. Lauterbur Research Center for Biomedical Imaging Shenzhen Institute of Advanced Technology Chinese Academy of Sciences Shenzhen518055 China University of Chinese Academy of Sciences Beijing100049 China Peng Cheng Laboratory Shenzhen518066 China Guangdong Provincial Key Laboratory of Artificial Intelligence in Medical Image Analysis and Application Guangzhou510080 China Faculty of Mathematical and Computer Sciences University of Gezira Wad Madani Sudan

Volumetric magnetic resonance (MR) image segmentation plays an important role in many clinical applications. Deep learning (DL) has recently achieved state-of-the-art or even human-level performance on various image segmentation tasks. Nevertheless, manually annotating volumetric MR images for DL model training is labor-exhaustive and time-consuming. In this work, we aim to train a semi-supervised and self-supervised collaborative learning framework for prostate 3D MR image segmentation while using extremely sparse annotations, for which the ground truth annotations are provided for just the central slice of each volumetric MR image. Specifically, semi-supervised learning and self-supervised learning methods are used to generate two independent sets of pseudo labels. These pseudo labels are then fused by Boolean operation to extract a more confident pseudo label set. The images with either manual or network self-generated labels are then employed to train a segmentation model for target volume extraction. Experimental results on a publicly available prostate MR image dataset demonstrate that, while requiring significantly less annotation effort, our framework generates very encouraging segmentation results. The proposed framework is very useful in clinical applications when training data with dense annotations are difficult to obtain. © 2022, CC BY-NC-ND.

关键词： Magnetic resonance

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Technical Phrase Extraction for Patent Mining: A Multi-level Approach

Technical Phrase Extraction for Patent Mining: A Multi-level...

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IEEE International Conference on Data Mining (ICDM)

作者： Ye Liu Han Wu Zhenya Huang Hao Wang Jianhui Ma Qi Liu Enhong Chen Hanqing Tao Ke Rui Anhui Province Key Laboratory of Big Data Analysis and Application School of Data Science & School of Computer Science and Technology University of Science and Technology of China

ISBN: (数字)9781728183169

ISBN: (纸本)9781728183176

Recent years have witnessed a booming increase of patent applications, which provides an open chance for revealing the inner law of innovation, but in the meantime, puts forward higher requirements on patent mining techniques. Considering that patent mining highly relies on patent document analysis, this paper makes a focused study on constructing a technology portrait for each patent, i.e., to recognize technical phrases concerned in it, which can summarize and represent patents from a technology angle. To this end, we first give a clear and detailed description about technical phrases in patents based on various prior works and analyses. Then, combining characteristics of technical phrases and multi-level structures of patent documents, we develop an Unsupervised Multi-level Technical Phrase Extraction (UMTPE) model. Particularly, a novel evaluation metric called Information Retrieval Efficiency (IRE) is designed to evaluate the extracted phrases from a new perspective, which greatly supplements traditional metrics like Precision and Recall. Finally, extensive experiments on real-world patent data show the effectiveness of our UMTPE model.

关键词： Measurement Patents Technological innovation Text analysis Conferences Information retrieval Data models

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An Adaptive Irregular Convolution U-Net for Reconstructing Ancient Channel of the Yellow River

An Adaptive Irregular Convolution U-Net for Reconstructing A...

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IEEE SENSORS

作者： Zhishun Guo Jianhui Zhao Ning Li Lin Wu College of Computer and Information Engineering Henan University Kaifeng China Henan. Key Laboratory of Big Data Analysis and Processing Henan University Kaifeng China Henan Engineering Research Center of Intelligent Technology and Application Henan University Kaifeng China

The lower reaches of the ancient Yellow River (AYR) migrated very frequently, like a loong swinging its tail on the land of China. This paper, based on Gaofen-3 full-polarization synthetic aperture radar (SAR) data, analyzed scattering characteristics of archaeolandscapes, which provided reliable evidences for the existence of AYR between Lankao and Sui County. Then, an adaptive irregular convolution kernel U-Net (AICK-U-Net) was proposed to reconstruct the part of channel of AYR, based on the image obtained by polarization decomposition, and the precision reached 96.21%. In summary, polarization decomposition and neural network in spaceborne SAR field, provided new insights for detecting and reconstructing archaeolandscapes.

关键词： Convolution Spaceborne radar Neural networks Scattering Sensors Rivers Reliability

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Understanding the predictability of path flow distribution in urban road networks using an information entropy approach

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Multimodal Transportation 2024年第2期3卷

作者： Guo, Bao Huang, Zhiren Zheng, Zhihao Zhang, Fan Wang, Pu School of Traffic and Transportation Engineering Rail Data Research and Application Key Laboratory of Hunan Province Central South University Changsha China Shandong Provincial Communications Planning and Design Institute Group Co. Ltd Jinan China Department of Computer Science Aalto University Helsinki Finland Department of Civil Engineering McGill University Montreal H3A 0C3 QC Canada Shenzhen Institutes of Advanced Technology Chinese Academy of Sciences Shenzhen China

Predicting the distributions of path flow between origin-destination (OD) pairs in an urban road network is crucial for developing efficient traffic control and management strategies. Here, we use the large-scale taxi GPS trajectory data of San Francisco and Shenzhen to study the predictability of path flow distribution in urban road networks. We develop an approach to project the time-varying path flow distributions into a high-dimensional space. In the high-dimensional space, information entropy is used to measure the predictability of path flow distribution. We find that the distributions of path flow between OD pairs are in general characterized with a high predictability. In addition, we analyze the factors affecting the predictability of path flow distribution. Finally, an n-gram model incorporating high-order gram and low-order gram is proposed to predict the distribution of path flow. A relatively high prediction accuracy is achieved. © 2024

关键词： Human mobility Information entropy Path flow distribution Predictability

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An efficient aggregate signature scheme for healthcare wireless sensor networks

Journal of Network Intelligence

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Journal of Network Intelligence 2021年第1期6卷 1-15页

作者： Chen, Jian-Neng Zhou, Yu-Ping Huang, Zhen-Jie Wu, Tsu-Yang Zou, Fu-Min Tso, Raylin Minnan Normal University Zhangzhou363000 China Key Laboratory of Data Science and Intelligence Application Zhangzhou363000 China Lab of Granular Computing Zhangzhou363000 China College of Computer Science and Engineering Shandong University of Science and Technology Qingdao266590 China Fujian Key Laboratory of Automotive Electronics and Electric Drive Fujian Provincial Key Laboratory of Big Data Mining and Applications Fujian University of Technology National Demonstration Center for Experimental Electronic Information and Electrical Technology Education Fujian University of Technology Fuzhou350008 China Department of Computer Science National Chengchi University Taipei Taiwan

In healthcare wireless sensor networks, there are a large number of sensors, which need to transmit a lot of information in real time. The aggregate signature scheme combines a great deal of signatures signed by different signers on different messages into one short signature, which greatly improves the efficiency of data transmission. In this paper, we present a scheme of certificate based aggregate signature, and prove its security in the random oracle model. The performance analysis shows that the scheme is efficient in sign and verify phase, and suitable for healthcare wireless sensor networks. © 2021, Taiwan Ubiquitous Information. All rights reserved.

关键词： Wireless sensor networks

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The ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC): Dataset, Tracks, Baseline and Results

The ISCSLP 2022 Intelligent Cockpit Speech Recognition Chall...

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International Symposium on Chinese Spoken Language Processing

作者： Ao Zhang Fan Yu Kaixun Huang Lei Xie Longbiao Wang Eng Siong Chng Hui Bu Binbin Zhang Wei Chen Xin Xu Audio Speech and Language Processing Group (ASLP@NPU) School of Computer Science Northwestern Polytechnical University Xi’an China Tianjin Key Laboratory of Cognitive Computing and Application College of Intelligence and Computing Tianjin University Tianjin China School of Computer Science and Engineering Nanyang Technological University Singapore Beijing Shell Shell Technology Co. Ltd Beijing China WeNet Open Source Community Li Auto Inc. Beijing China

ISBN: (纸本)9798350397970

This paper summarizes the outcomes from the ISCSLP 2022 Intelligent Cockpit Speech Recognition Challenge (ICSRC). We first address the necessity of the challenge and then introduce the associated dataset collected from a new-energy vehicle (NEV) covering a variety of cockpit acoustic conditions and linguistic contents. We then describe the track arrangement and the baseline system. Specifically, we set up two tracks in terms of allowed model/system size to investigate resource-constrained and -unconstrained setups, targeting to vehicle embedded as well as cloud ASR systems respectively. Finally we summarize the challenge results and provide the major observations from the submitted systems.

关键词： Measurement Industries Visualization Target tracking Speech recognition Linguistics Acoustics

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A Novel Image Encryption Scheme Based on Poker Cross-Shuffling and Fractional Order Hyperchaotic System

A Novel Image Encryption Scheme Based on Poker Cross-Shuffli...

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作者： Zhong Chen Huihuang Zhao Junyao Chen College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Department of Computer Engineering Ajou University

A novel digital image encryption scheme based on Poker shuffling and fractional order hyperchaotic system is proposed. Poker intercrossing operation has nonlinearity and periodicity, and can form permutation group. Firstly, the plain image is transformed into block image, and each block is confused by Poker shuffling operation with key. Secondly, bit plane of every pixel in each block is confused. Lastly, the scrambled image is encrypted by fractional order hyperchaotic sequence. This encryption scheme provides a secure and efficient key stream, and guarantees a large key space. The experimental results and security analysis imply that the new encryption method has secure encryption effect and property of resisting common attacks.

关键词： Image encryption algorithm Poker shuffling operation Fractional order hyperchaotic system

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Outer Synchronization Between Complex Delayed Networks with Both Uncertain Parameters and Unknown Topological Structure

Outer Synchronization Between Complex Delayed Networks with ...

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作者： Zhong Chen Xiaomei Tian Tianqi Lei Junyao Chen College of Computer Science and Technology Hengyang Normal University Hunan Provincial Key Laboratory of Intelligent Information Processing and Application Department of Computer Engineering Ajou University

In this paper,synchronization of both non-identical unknown network and identical known network can be *** on Lyapunov stability theory,for the case of non-identical or identical network,

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