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

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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IF2Net: Innately Forgetting-Free Networks for Continual Learning

arXiv

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arXiv 2023年

作者： Li, Depeng Wang, Tianqi Xu, Bingrong Kawaguchi, Kenji Zeng, Zhigang Suganthan, Ponnuthurai Nagaratnam The School of Artificial Intelligence and Automation The Institute of Artificial Intelligence Huazhong University of Science and Technology The Key Laboratory of Image Processing and Intelligent Control Education Ministry of China Wuhan430074 China The School of Automation Wuhan University of Technology Wuhan430070 China The School of Computing National University of Singapore 117417 Singapore The School of Electrical and Electronic Engineering Nanyang Technological University Singapore639798 Singapore The KINDI Center for Computing Research College of Engineering Qatar University Doha Qatar

Continual learning can incrementally absorb new concepts without interfering with previously learned knowledge. Motivated by the characteristics of neural networks, in which information is stored in weights on connections, we investigated how to design an Innately Forgetting-Free Network (IF2Net) for continual learning context. This study proposed a straightforward yet effective learning paradigm by ingeniously keeping the weights relative to each seen task untouched before and after learning a new task. We first presented the novel representation-level learning on task sequences with random weights. This technique refers to tweaking the drifted representations caused by randomization back to their separate task-optimal working states, but the involved weights are frozen and reused (opposite to well-known layer-wise updates of weights). Then, sequential decision-making without forgetting can be achieved by projecting the output weight updates into the parsimonious orthogonal space, making the adaptations not disturb old knowledge while maintaining model plasticity. IF2Net allows a single network to inherently learn unlimited mapping rules without telling task identities at test time by integrating the respective strengths of randomization and orthogonalization. We validated the effectiveness of our approach in the extensive theoretical analysis and empirical study. Copyright © 2023, The Authors. All rights reserved.

关键词： Decision making

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Cross-receptive Focused Inference Network for Lightweight image Super-Resolution

arXiv

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arXiv 2022年

作者： Li, Wenjie Li, Juncheng Gao, Guangwei Deng, Weihong Zhou, Jiantao Yang, Jian Qi, Guo-Jun The Intelligent Visual Information Perception Laboratory Institute of Advanced Technology Nanjing University of Posts and Telecommunications Nanjing210046 China The Provincial Key Laboratory for Computer Information Processing Technology Soochow University Suzhou215006 China The School of Communication and Information Engineering Shanghai University Shanghai200444 China Jiangsu Key Laboratory of Image and Video Understanding for Social Safety Nanjing University of Science and Technology Nanjing210094 China The Pattern Recognition and Intelligent System Laboratory School of Artificial Intelligence Beijing University of Posts and Telecommunications Beijing100876 China The State Key Laboratory of Internet of Things for Smart City Department of Computer and Information Science Faculty of Science and Technology University of Macau 999078 China The School of Computer Science and Technology Nanjing University of Science and Technology Nanjing210094 China The Research Center for Industries of the Future The School of Engineering Westlake University Hangzhou310024 China OPPO Research SeattleWA98101 United States

Recently, Transformer-based methods have shown impressive performance in single image super-resolution (SISR) tasks due to the ability of global feature extraction. However, the capabilities of Transformers that need to incorporate contextual information to extract features dynamically are neglected. To address this issue, we propose a lightweight Cross-receptive Focused Inference Network (CFIN) that consists of a cascade of CT Blocks mixed with CNN and Transformer. Specifically, in the CT block, we first propose a CNN-based Cross-Scale Information Aggregation Module (CIAM) to enable the model to better focus on potentially helpful information to improve the efficiency of the Transformer phase. Then, we design a novel Cross-receptive Field Guided Transformer (CFGT) to enable the selection of contextual information required for reconstruction by using a modulated convolutional kernel that understands the current semantic information and exploits the information interaction within different self-attention. Extensive experiments have shown that our proposed CFIN can effectively reconstruct images using contextual information, and it can strike a good balance between computational cost and model performance as an efficient model. Source codes will be available at https://***/IVIPLab/CFIN. Copyright © 2022, The Authors. All rights reserved.

关键词： Semantics

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Predicting lung nodule malignancies by combining deep convolutional neural network and handcrafted features

arXiv

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arXiv 2018年

作者： Li, Shulong Xu, Panpan Li, Bin Chen, Liyuan Zhou, Zhiguo Hao, Hongxia Duan, Yingying Folkert, Michael Ma, Jianhua Huang, Shiying Jiang, Steve Wang, Jing School of Biomedical Engineering Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China Longgang District People's Hospital Shenzhen518172 China Department of Radiation Oncology University of Texas Southwestern Medical Center DallasTX75235 United States School of Computer Science and Technology Xidian University Xi'an710071 China School of Traditional Chinese Medicine Southern Medical University Guangzhou510515 China

To predict lung nodule malignancy with a high sensitivity and specificity, we propose a fusion algorithm that combines handcrafted features (HF) into the features learned at the output layer of a 3D deep convolutional neural network (CNN). First, we extracted twenty-nine handcrafted features, including nine intensity features, eight geometric features, and twelve texture features based on grey-level co-occurrence matrix (GLCM) averaged from five grey levels, four distances and thirteen directions. We then trained 3D CNNs modified from three 2D CNN architectures (AlexNet, VGG-16 Net and Multi-crop Net) to extract the CNN features learned at the output layer. For each 3D CNN, the CNN features combined with the 29 handcrafted features were used as the input for the support vector machine (SVM) coupled with the sequential forward feature selection (SFS) method to select the optimal feature subset and construct the classifiers. The fusion algorithm takes full advantage of the handcrafted features and the highest level CNN features learned at the output layer. It can overcome the disadvantage of the handcrafted features that may not fully reflect the unique characteristics of a particular lesion by combining the intrinsic CNN features. Meanwhile, it also alleviates the requirement of a large scale annotated dataset for the CNNs based on the complementary of handcrafted features. The patient cohort includes 431 malignant nodules and 795 benign nodules extracted from the LIDC/IDRI database. For each investigated CNN architecture, the proposed fusion algorithm achieved the highest AUC, accuracy, sensitivity, and specificity scores among all competitive classification models. Copyright © 2018, The Authors. All rights reserved.

关键词： Deep neural networks

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Target state estimation based on particle filter

引用

Yi Qi Yi Biao Xue Bao/Chinese Journal of Scientific Instrument 2011年第12 SUPPL.期32卷 36-40页

作者： Zhao, Yongting Xiao, Yanghui Tong, Xinxin Zhu, Dan Shenyang Institute of Automation Chinese Acad. of Sci. Shenyang 110016 China Key Laboratory of Optical-Electronics Information Processing Chinese Acad. of Sci. Shenyang 110016 China Key Laboratory of Image Understanding and Computer Vision Liaoning Province Shenyang 110016 China Graduate School of Chinese Acad. of Sci. Beijing 100049 China

A state estimation method based on the particle filter is proposed, and the current statistical model is used. A reasonable method, that discrete the noise distribution coefficient, is proposed. It enhances the state estimation performance of the maneuvering target. Experiments show the effectiveness of the method. In the Kalman filter framework USES the current statistical model of state estimation methods are compared. The experimental results show that this method can not only approximate the optimal estimate, but also has strong robustness of the maneuverability.

关键词： State estimation

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Detection and Tracking of Multiple Mice Using Part Proposal Networks

arXiv

引用

arXiv 2019年

作者： Jiang, Zheheng Liu, Zhihua Chen, Long Tong, Lei Zhang, Xiangrong Lan, Xiangyuan Crookes, Danny Yang, Ming-Hsuan Zhou, Huiyu School of Computing and Communication Lancaster University United Kingdom School of Computing and Mathematical Sciences University of Leicester United Kingdom The Key Laboratory of Intelligent Perception and Image Understanding of Ministry of Education Xidian University China The Shenzhen NS-Tech Co.Ltd. China School of Electronics Electrical Engineering and Computer Science Queen's University Belfast United Kingdom The School of Engineering University of California at Merced MercedCA United States

The study of mouse social behaviours has been increasingly undertaken in neuroscience research. However, automated quantification of mouse behaviours from the videos of interacting mice is still a challenging problem, where object tracking plays a key role in locating mice in their living spaces. Artificial markers are often applied for multiple mice tracking, which are intrusive and consequently interfere with the movements of mice in a dynamic environment. In this paper, we propose a novel method to continuously track several mice and individual parts without requiring any specific tagging. Firstly, we propose an efficient and robust deep learning based mouse part detection scheme to generate part candidates. Subsequently, we propose a novel Bayesian-inference Integer Linear Programming Model that jointly assigns the part candidates to individual targets with necessary geometric constraints whilst establishing pair-wise association between the detected parts. There is no publicly available dataset in the research community that provides a quantitative test-bed for the part detection and tracking of multiple mice, and we here introduce a new challenging Multi-Mice PartsTrack dataset that is made of complex behaviours. Finally, we evaluate our proposed approach against several baselines on our new datasets, where the results show that our method outperforms the other state-of-the-art approaches in terms of accuracy. We also demonstrate the generalization ability of the proposed approach on tracking zebra and locust. Copyright © 2019, The Authors. All rights reserved.

关键词： Deep learning

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A Born-Again Takagi-Sugeno-Kang Fuzzy Classifier with Decoupled Fuzzy Dark Knowledge Distillation

SSRN

引用

SSRN 2024年

作者： Zhang, Xiongtao Yin, Zezong Jiang, Yunliang Jiang, Yizhang Sun, Danfeng Liu, Yong School of Information Engineering Huzhou University Huzhou31300 China Zhejiang Key Laboratory of Intelligent Education Technology and Application Zhejiang Normal University Jinhua321004 China School of Computer Science and Technology Zhejiang Normal University Jinhua321004 China School of Artificial Intelligence and Computer Science Jiangnan University Wuxi 214122 China Key Laboratory of Image Processing and Intelligent Control Huazhong University of Science and Technology Ministry of Education Wuhan430074 China School of Computer Science Hangzhou Dianzi University Hangzhou310018 China College of Control Science and Engineering Zhejiang University Hangzhou310027 China

In order to enpower the hight performance as well as interpretability of low-order TSK fuzzy classifier, a born-again TSK fuzzy classier embedded with decoupled fuzzy dark knowledge distillation called HTSK-LLM-DKD is proposed in this study. HTSK-LLM DKD achieves the following distinctive characteristics: 1) It takes high-order TSK as teacher model and low-order TSK fuzzy classifier as student model, and leverages the proposed Least Learning Machine based Decoupling Knowledge Distillation to distill the fuzzy dark knowledge from high-order TSK fuzzy classifier to low-order TSK fuzzy classifier, which resulting in low-order TSK fuzzy classifier endowed with enhanced performance surpassing or at least comparable to high-order TSK fuzzy classifier;specifically 2) The Negative Euclidean distance between the output of teacher model and each class is employed to obtain the teacher logits, and then it computes teacher/student soft labels by the softmax function with distilling temperature parameter;3) By reformulating the Kullback Leibler divergence, it decouples fuzzy dark knowledge into target class knowledge and non-target class knowledge, and transfers them to student model, which taking HTSK-LLM-DKD with outstanding performance and relatively fast running speed. Extensive experiment results on UCI benchmarking datasets and real-world dataset Cleveland Heart Disease demonstrate the effectiveness of the proposed HTSK-LLM-DKD. © 2024, The Authors. All rights reserved.

关键词： Students

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Global Consensus for Heterogeneous Saturated Multi-Agent Systems via Sampled-Data Control

Global Consensus for Heterogeneous Saturated Multi-Agent Sys...

引用

IEEE Conference on Decision and Control

作者： Juan Qian Xiaoling Wang Daniele Astolfi Housheng Su Guo-Ping Jiang College of Automation and the College of Artificial Intelligence Nanjing University of Posts and Telecommunications Nanjing China Jiangsu Engineering Center for IOT Intelligent Robots(IOTRobot) Nanjing China CNRS LAGEPP UMR 5007 43 boulevard du 11 novembre 1918 Université Claude Bernard Lyon 1 Villeurbanne France Key Laboratory of Smart Manufacturing in Energy Chemical Process Ministry of Education East China University of Science and Technology Shanghai China School of Artificial Intelligence and Automation Image Processing and Intelligent Control Key Laboratory of Education Ministry of China Huazhong University of Science and Technology Luoyu Road 1037 Wuhan China

ISBN: (数字)9798350316339

ISBN: (纸本)9798350316346

This paper addresses the global consensus problem for multi-input multi-output saturated systems within a sampled-data framework, aiming to advance global consensus, manage heterogeneous actuator saturation across components, and preserve distributed characteristics by using sampled-data feedback. We propose a distributed control algorithm that incorporates a redesigned saturation function, represented as decentralized dynamic saturation levels. These levels for each agent’s dimensions are autonomously updated through an adaptive strategy, which mitigates heterogeneous saturation by carefully selecting constant and time-varying saturation parameters in it. Lyapunov analysis proves that global consensus can be achieved under the proposed control law, provided the sampling periods of all agents remain below a calculated threshold. An example is given to demonstrate the effectiveness of this approach.

关键词： Actuators Upper bound Heuristic algorithms Decentralized control Multi-agent systems

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SU-G-206-16: Investigation of Dosimetric Consequence Via Cone-Beam CT Based Dose Reconstruction in Hepatocellular Carcinoma Radiotherapy

引用

Medical Physics 2016年第6PART25期43卷 3643-3643页

作者： P Huang H Li J Chen C Ma Y Gang S Qin Y Yin D Li Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University China Department of Radiation Oncology Shandong Cancer Hospital and Institute China

Purpose: Many patients with technically unresectable or medically inoperable hepatocellular carcinoma (HCC) had hepatic dosimetric variations as a result of inter-fraction anatomical deformation. This study was conducted to assess the hepatic dosimetric consequences via reconstructing weekly dose in HCC patients receiving three dimensional conformal radiation therapy. Methods: Twenty-one HCC patients with 21 planning CT (pCT) scans and 63 weekly Cone-beam CT (CBCT) scans were enrolled in this investigation. Among them, six patients had been diagnosed of radiation induced liver disease (RILD) and the other fifteen patients had good prognosis after treatment. And each patient had three weekly CBCT before re-planning. In reconstructing CBCT-based weekly dose, we registered pCT to CBCT to provide the correct Hounsfield units for the CBCT using gradient-based deformable image registration (DIR), and this modified CBCT (mCBCT) were introduced to enable dose *** obtain the weekly dosimetric consequences, the initial plan beam configurations and dose constraints were re-applied to mCBCT for performing dose calculation, and the mCBCT were extrapolated to 25 fractions. Besides, the manually delineated contour was propagated automatically onto the mCBCT of the new patient by exploiting the deformation vectors field, and the reconstructed weekly dose was mapped back to pCT to understand the dose distribution difference. Also, weekly dosimetric variations were compared with the hepatic radiation tolerance in terms of D50 and Dmean. Results: Among the twenty-one patients, the three weekly D50 increased by 0.7Gy, 5.1Gy and 6.1Gy, respectively, and Dmean increased by 0.9%, 4.7% and 5.5%, respectively. For patients with RILD, the average values of the third weekly D50 and Dmean were both high than hepatic radiation tolerance, while the values of patients without RILD were below. Conclusion: The planned dose on pCT was not a real dose to the liver, and the liver overdose i

关键词： Dosimetry Cone beam computed tomography Liver Cancer Radiation treatment Conformal radiation therapy Medical image reconstruction Three dimensional image processing

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A high-speed high-density array-multiplier-accumulator for digital signal processing

引用

ELECTRONICS AND communicationS IN JAPAN PART II-ELECTRONICS 1996年第2期79卷 86-97页

作者： Minami, T Kasai, R Matsuda, H Kusaba, R Member NTT LSI Laboratories Atsugi Japan 243-01 Graduated in 1980 from the Department of Electrical Engineering Kyushu University where he received his Master's degree in 1982 and joined NTT. Until March 1986 he was engaged in the development of application software for electronic switching systems. He then engaged in research and development of LSIs for image signal processing. At present he is Senior Research Engineer Advanced LSI Laboratory NTT LSI Laboratories. He is a member of IEEE. Graduated in 1972 from the Department of Electrical Engineering Osaka University where he received his Master's degree in 1974 and his Ph.D. in 1992. He joined NTT in 1974. He is engaged in research and development of analysis of MOS devices and design of ASIC for communication. At present he is Executive Research Engineer Advanced LSI Laboratory NTT LSI Laboratories. He is a member of IEEE. Graduated in 1987 from the Department of Communications Tohoku University where he received his Master's degree in 1989 and joined NTT. He is engaged in research and development of high-speed design for image processing LSIs. At present he is Research Engineer Advanced LSI Laboratory NTT LSI Laboratories. He is a member of the Information Processing Society. Graduated in 1985 from the Department of Electrical Engineering Keio University where he received his Master's degree in 1987 and joined NTT. He is engaged in CAD research. At present he is Senior Research Engineer Advanced LSI Laboratory NTT LSI Laboratories. He is a member of the Information Processing Society and IEEE.

This paper discusses the downsizing and speed improvement of short-word multiplier-accumulators, which are frequently used in digital signal processors. As a first step, the optimal configuration for an array-type carry-save adder is considered where the shortest path in the full-adder is used to propagate the sum signal and the carry signal is sent to the full-adder of the two lower stages by skipping a stage. A configuration of the full-adder suitable for the structure is proposed. The case of eight partial product additions shows that the delay can be reduced by 22 percent compared to a simple array-type carry-save adder. Then the short-word carry look-ahead adder using the pass-transistor logic is considered. It is shown that a single-stage carry look-ahead circuit with a four-bitwise iterative structure exhibits nearly the same delay as a two-stage carry look-ahead circuit. In other words, the former is better suited to downsizing. This paper intends to examine the effectiveness of the foregoing new array-type carry-save adder and the single-stage carry look-ahead circuit using the 0.5-mu m CMOS technology. A 16-bit x 14-bit + 31-bit multiplier-accumulator has been designed and is evaluated for cases where the array-type carry-save adder is used to handle accumulation as well as partial products. The resulting area and delay are 0.77 x 0.78 mm(2) and 6.8 ns, respectively. The effectiveness of the approach used in this paper is evaluated by constructing a multiplier-accumulator, but the method is also useful in constructing a multiplier.

关键词： product-sum operation array multiplier carry-save adder full-adder carry look-ahead adder

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