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Multimodal Fusion of Brain Imaging Data: Methods and Applications

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Machine Intelligence Research 2024年第1期21卷 136-152页

作者： Na Luo Weiyang Shi Zhengyi Yang Ming Song Tianzi Jiang brainnetome center and national laboratory of pattern recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China center for Excellence in Brain Science and Intelligence Technology Institute of AutomationChinese Academy of SciencesBeijing 100190China Research center for Augmented Intelligence Zhejiang LaboratoryHangzhou 311100China

Neuroimaging data typically include multiple modalities,such as structural or functional magnetic resonance imaging,dif-fusion tensor imaging,and positron emission tomography,which provide multiple views for observing and analyzing the *** lever-age the complementary representations of different modalities,multimodal fusion is consequently needed to dig out both inter-modality and intra-modality *** the exploited rich information,it is becoming popular to combine multiple modality data to ex-plore the structural and functional characteristics of the brain in both health and disease *** this paper,we first review a wide spectrum of advanced machine learning methodologies for fusing multimodal brain imaging data,broadly categorized into unsupervised and supervised learning *** by this,some representative applications are discussed,including how they help to under-stand the brain arealization,how they improve the prediction of behavioral phenotypes and brain aging,and how they accelerate the biomarker exploration of brain ***,we discuss some exciting emerging trends and important future ***,we intend to offer a comprehensive overview of brain imaging fusion methods and their successful applications,along with the chal-lenges imposed by multi-scale and big data,which arises an urgent demand on developing new models and platforms.

关键词： Multimodal fusion supervised learning unsupervised learning brain atlas cognition brain disorders

Boosting Multi-modal Ocular recognition via Spatial Feature Reconstruction and Unsupervised Image Quality Estimation

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Machine Intelligence Research 2024年第1期21卷 197-214页

作者： Zihui Yan Yunlong Wang Kunbo Zhang Zhenan Sun Lingxiao He center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China JD AI Research Beijing 100176China

In the daily application of an iris-recognition-at-a-distance(IAAD)system,many ocular images of low quality are *** the iris part of these images is often not qualified for the recognition requirements,the more accessible periocular regions are a good complement for *** further boost the performance of IAAD systems,a novel end-to-end framework for multi-modal ocular recognition is *** proposed framework mainly consists of iris/periocular feature extraction and matching,unsupervised iris quality assessment,and a score-level adaptive weighted fusion ***,ocular feature reconstruction(OFR)is proposed to sparsely reconstruct each probe image by high-quality gallery images based on proper feature ***,a brand new unsupervised iris quality assessment method based on random multiscale embedding robustness is *** from the existing iris quality assess-ment methods,the quality of an iris image is measured by its robustness in the embedding *** last,the fusion strategy exploits the iris quality score as the fusion weight to coalesce the complementary information from the iris and periocular *** experi-mental results on ocular datasets prove that the proposed method is obviously better than unimodal biometrics,and the fusion strategy can significantly improve therecognition performance.

关键词： Iris recognition periocular recognition spatial feature reconstruction fully convolutional network flexible matching unsupervised iris quality assessment adaptive weight fusion

CASIA-Iris-Africa:A Large-scale African Iris Image Database

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Machine Intelligence Research 2024年第2期21卷 383-399页

作者： Jawad Muhammad Yunlong Wang Junxing Hu Kunbo Zhang Zhenan Sun School of Artificial Intelligence University of Chinese Academy of SciencesBeijing 100049China center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China

Iris biometrics is a phenotypic biometric trait that has proven to be agnostic to human natural physiological *** on iris biometrics has progressed tremendously,partly due to publicly available iris *** databases have been available to researchers that address pressing iris biometric challenges such as constraint,mobile,multispectral,synthetics,long-distance,contact lenses,liveness detection,***,these databases mostly contain subjects of Caucasian and Asian docents with very few *** many investigative studies on racial bias in face biometrics,very few studies on iris biometrics have been published,mainly due to the lack of racially diverse large-scale databases containing sufficient iris samples of Africans in the public ***,most of these databases contain a relatively small number of subjects and labelled *** paper proposes a large-scale African database named Chinese Academy of Sciences Institute of Automation(CASIA)-Iris-Africa that can be used as a complementary database for the iris recognition community to mediate the effect of racial biases on *** database contains 28717 images of 1023 African subjects(2046 iris classes)with age,gender,and ethnicity attributes that can be useful in demographically sensitive studies of *** of specific application protocols are incorporated with the database to ensure the database’s variability and *** results of some open-source state-of-the-art(SOTA)algorithms on the database are presented,which will serve as baseline *** relatively poor performances of the baseline algorithms on the proposed database despite better performance on other databases prove that racial biases exist in these iris recognition algorithms.

关键词： African iris recognition racial bias iris image database biometrics iris recognition

Uncovering hidden nodes and hidden links in complex dynamic networks

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Science China(Physics,Mechanics & Astronomy) 2024年第4期67卷 51-63页

作者： Zhaoyang Zhang Xinyu Wang Haihong Li Yang Chen Zhilin Qu Yuanyuan Mi Gang Hu Department of Physics School of Physical Science and TechnologyNingbo UniversityNingbo 315211China School of Science Beijing University of Posts and TelecommunicationsBeijing 100876China Department of Medicine David Geffen School of MedicineUniversity of CaliforniaLos Angeles 90095USA brainnetome center and national laboratory of pattern recognition Institute of AutomationChinese Academy of SciencesBeijing 100190China Department of Psychology Tsinghua UniversityBeijing 100084China Department of Physics Beijing Normal UniversityBeijing 100875China

Inferring network structures from available data has attracted much interest in network science;however,in many realistic networks,only some of the nodes are perceptible while others are hidden,making it a challenging *** this work,we develop a method for reconstructing the network with hidden nodes and links,taking account of fast-varying noise and time-delay *** calculating the correlations of available data with different derivative orders for multiple pairs of accessible nodes,analyzing and integrating the relationships between different correlations,and defining diverse hidden-node-related reconstruction motifs,we can effectively identify the hidden nodes and hidden links in the network.

关键词： networks and genealogical trees stochastic analysis methods time series analysis

Comprehensive Relation Modelling for Image Paragraph Generation

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Machine Intelligence Research 2024年第2期21卷 369-382页

作者： Xianglu Zhu Zhang Zhang Wei Wang Zilei Wang Automation Department University of Science and Technology of ChinaHefei 230027China center for Research on Intelligent Perception and Computing National Laboratory of Pattern RecognitionInstitute of AutomationChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100864China

Image paragraph generation aims to generate a long description composed of multiple sentences,which is different from traditional image captioning containing only one *** of previous methods are dedicated to extracting rich features from image regions,and ignore modelling the visual *** this paper,we propose a novel method to generate a paragraph by modelling visual relationships ***,we parse an image into a scene graph,where each node represents a specific object and each edge denotes the relationship between two ***,we enrich the object features by implicitly encoding visual relationships through a graph convolutional network(GCN).We further explore high-order relations between different relation features using another graph convolutional *** addition,we obtain the linguistic features by projecting the predicted object labels and their relationships into a semantic embedding *** these features,we present an attention-based topic generation network to select relevant features and produce a set of topic vectors,which are then utilized to generate multiple *** evaluate the proposed method on the Stanford image-paragraph dataset which is currently the only available dataset for image paragraph generation,and our method achieves competitive performance in comparison with other state-of-the-art(SOTA)methods.

关键词： Image paragraph generation visual relationship scene graph graph convolutional network(GCN) long short-term memory

PHUI-GA: GPU-based efficiency evolutionary algorithm for mining high utility itemsets

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Journal of Systems Engineering and Electronics 2024年第4期35卷 965-975页

作者： JIANG Haipeng WU Guoqing SUN Mengdan LI Feng SUN Yunfei FANG Wei Jiangsu Provincial Engineering laboratory of pattern recognition and Computational Intelligence Department of Computer Science and TechnologyJiangnan UniversityWuxi 214122China China Ship Scientific Research center Wuxi 214082China Taihu laboratory of Deepsea Technological Science Wuxi 214082China Department of Mathematics Nanjing UniversityNanjing 210023China

Evolutionary algorithms(EAs)have been used in high utility itemset mining(HUIM)to address the problem of discover-ing high utility itemsets(HUIs)in the exponential search *** have good running and mining performance,but they still require huge computational resource and may miss many *** to the good combination of EA and graphics processing unit(GPU),we propose a parallel genetic algorithm(GA)based on the platform of GPU for mining HUIM(PHUI-GA).The evolution steps with improvements are performed in central processing unit(CPU)and the CPU intensive steps are sent to GPU to eva-luate with multi-threaded *** show that the mining performance of PHUI-GA outperforms the existing *** mining 90%HUIs,the PHUI-GA is up to 188 times better than the existing EAs and up to 36 times better than the CPU parallel approach.

关键词： high utility itemset mining(HUIM) graphics process-ing unit(GPU)parallel genetic algorithm(GA) mining perfor-mance

Depth-Guided Vision Transformer With Normalizing Flows for Monocular 3D Object Detection

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IEEE/CAA Journal of Automatica Sinica 2024年第3期11卷 673-689页

作者： Cong Pan Junran Peng Zhaoxiang Zhang the center for Research on Intelligent Perception and Computing(CRIPAC) National Laboratory of Pattern Recognition(NLPR)Institute of AutomationChinese Academy of Sciences(CASIA)Beijing 100190 the School of Future Technology University of Chinese Academy of Sciences(UCAS)Beijing 100049China the Huawei Inc. Beijing 100085China IEEE the Institute of Automation Chinese Academy of Sciences(CASIA)Beijing 100190 the University of Chinese Academy of Sciences(UCAS) Beijing 100049 the Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science&Innovation Chinese Academy of Sciences(HKISI CAS)Hong Kong 999077China

Monocular 3D object detection is challenging due to the lack of accurate depth *** methods estimate the pixel-wise depth maps from off-the-shelf depth estimators and then use them as an additional input to augment the RGB ***-based methods attempt to convert estimated depth maps to pseudo-LiDAR and then use LiDAR-based object detectors or focus on the perspective of image and depth fusion ***,they demonstrate limited performance and efficiency as a result of depth inaccuracy and complex fusion mode with *** from these approaches,our proposed depth-guided vision transformer with a normalizing flows(NF-DVT)network uses normalizing flows to build priors in depth maps to achieve more accurate depth *** we develop a novel Swin-Transformer-based backbone with a fusion module to process RGB image patches and depth map patches with two separate branches and fuse them using cross-attention to exchange information with each ***,with the help of pixel-wise relative depth values in depth maps,we develop new relative position embeddings in the cross-attention mechanism to capture more accurate sequence ordering of input *** method is the first Swin-Transformer-based backbone architecture for monocular 3D object *** experimental results on the KITTI and the challenging Waymo Open datasets show the effectiveness of our proposed method and superior performance over previous counterparts.

关键词： Monocular 3D object detection normalizing flows Swin Transformer

Phenotype-Directed DNA Nanomachine for in Situ Analysis of Stem Cell-like Subpopulation in Breast Cancer

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CCS Chemistry 2024年第4期6卷 1034-1046页

作者： Ya Cao Huiru Mao Lingjun Sha Qi Liu Bing Han Jing Zhao Genxi Li center for Molecular recognition and Biosensing Shanghai Engineering Research Center of Organ RepairSchool of Life SciencesShanghai UniversityShanghai 200444 State Key laboratory of Analytical Chemistry for Life Science School of Life SciencesNanjing UniversityNanjing 210023

The cancer stem cell hypothesis provides a basis for prediction of the recurrence and risk of metastasis in breast ***,the unique expression pattern of stemness markers and the presence of nonstem-like cancer cells with varied phenotypes have brought great challenges to the characterization of breast cancer stem *** address these challenges,a phenotype-directed DNA nanomachine has been designed for high-accuracy labeling and in situ analysis of the stem cell-like subpopulation in breast *** key for the design is to use cell surfaceanchored inputs to activate the nanomachine,which undergoes different branch migration pathways such that the signal strand can only be brought onto the cancer cells having the stem cell-like *** sensitive determination and single-step isolation of the stem cell-like subpopulation were achieved by incorporating functional groups into the signal strand such that the nanomachine was successfully applied in a tumor-bearing mouse ***,the approach provides for a substantial improvement in capability for the analysis of the breast cancer stem cell-like subpopulation,and it is expected that the new approach will advance the use of DNA nanomachines in cancer-related studies.

关键词： breast cancer DNA nanomachine stem cell-like subpopulation electrochemical determination single-step isolation phenotypic analysis

Organic heterojunction synaptic device with ultra high recognition rate for neuromorphic computing

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Nano Research 2024年第6期17卷 5614-5620页

作者： Xuemeng Hu Jialin Meng Tianyang Feng Tianyu Wang Hao Zhu Qingqing Sun David Wei Zhang Lin Chen School of Microelectronics State Key Laboratory of Integrated Chips and SystemsFudan UniversityShanghai 200433China national Integrated Circuit Innovation center Shanghai 201203China

Traditional computing structures are blocked by the von Neumann bottleneck,and neuromorphic computing devices inspired by the human brain which integrate storage and computation have received more and more ***,a flexible organic device with 2,7-dioctyl[1]benzothieno[3,2-b][1]benzothiophene(C8-BTBT)and 2,9-didecyldinaphtho[2,3-b:2′,3′-f]thieno[3,2-b]thiophene(C10-DNTT)heterostructural channel having excellent synaptic behaviors was fabricated on muscovite(MICA)substrate,which has a memory window greater than 20 *** device shows better electrical characteristics than organic field effect transistors with single organic semiconductor ***,the device simulates organism synaptic behaviors successfully,such as paired-pulse facilitation(PPF),long-term potentiation/depression(LTP/LTD)process,and transition from short-term memory(STM)to long-term memory(LTM)by optical and electrical ***,the neuromorphic computing function was verified using the Modified national Institute of Standards and Technology(MNIST)pattern recognition,with a recognition rate nearly 100%without *** research proposes a flexible organic heterojunction with the ultra-high recognition rate in MNIST pattern recognition and provides the possibility for future flexible wearable neuromorphic computing devices.

关键词： organic heterojunction neuromorphic computing synapse behaviors optical modulation Modified national Institute of Standards and Technology(MNIST)pattern recognition