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Multi-View Imputation and Cross-Attention Network Based on Incomplete Longitudinal and Multimodal Data for Conversion Prediction of Mild Cognitive Impairment

arXiv

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arXiv 2022年

作者： Wang, Tao Chen, Xiumei Zhang, Xiaoling Zhou, Shuoling Feng, Qianjin Huang, Meiyan School of Biomedical Engineering Southern Medical University Guangzhou510515 China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangzhou510515 China Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangzhou510515 China

Predicting whether subjects with mild cognitive impairment (MCI) will convert to Alzheimer’s disease is a significant clinical challenge. Longitudinal variations and complementary information inherent in longitudinal and multimodal data are crucial for MCI conversion prediction, but persistent issue of missing data in these data may hinder their effective application. Additionally, conversion prediction should be achieved in the early stages of disease progression in clinical practice, specifically at baseline visit (BL). Therefore, longitudinal data should only be incorporated during training to capture disease progression information. To address these challenges, a multi-view imputation and cross-attention network (MCNet) was proposed to integrate data imputation and MCI conversion prediction in a unified framework. First, a multi-view imputation method combined with adversarial learning was presented to handle various missing data scenarios and reduce imputation errors. Second, two cross-attention blocks were introduced to exploit the potential associations in longitudinal and multimodal data. Finally, a multi-task learning model was established for data imputation, longitudinal classification, and conversion prediction tasks. When the model was appropriately trained, the disease progression information learned from longitudinal data can be leveraged by BL data to improve MCI conversion prediction at BL. MCNet was tested on two independent testing sets and single-modal BL data to verify its effectiveness and flexibility in MCI conversion prediction. Results showed that MCNet outperformed several competitive methods. Moreover, the interpretability of MCNet was demonstrated. Thus, our MCNet may be a valuable tool in longitudinal and multimodal data analysis for MCI conversion prediction. Codes are available at https://***/Meiyan88/MCNET. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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How Intermolecular Interaction and Hydrostatic Pressure Affect the Organic Room Temperature Phosphorescence Properties: A Qm/Mm Study

SSRN

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SSRN 2024年

作者： Xu, Yuanyuan Pang, Kunwei Song, Yuzhi Lin, Lili Wang, Chuan-Kui Fan, Jianzhong Shandong Province Key Laboratory of Medical Physics and Image Processing Technology School of Physics and Electronics Shandong Normal University Jinan250014 China School of Science Qilu University of Technology Shandong Academy of Sciences Jinan250353 China State Key Laboratory of Luminescent Materials and Devices South China University of Technology China

Stimulus-responsive organic room temperature phosphorescence (RTP) materials with luminescence properties (such as color, efficiency and lifetime) changed with force, heat, light, electric filed, etc., have drawn increasing attention for their broad applications in the fields of sensors, bioimaging and organic light emitting diodes (OLEDs). Especially, the hydrostatic pressure effects on excited state properties of RTP molecules are conspicuous. However, the relationship among structure, hydrostatic pressure and RTP properties is unclear, corresponding theoretical investigations are highly desired. Herein, on the basis of density functional theory (DFT) and time-dependent density functional theory (TD-DFT), combined with the quantum mechanics/molecular mechanics (QM/MM) and the thermal vibration correlation function (TVCF) methods, the influence of methyl substitution effect and hydrostatic pressure effect on the photophysical properties of five isomers (O-PTZ-1Me-H (ax), O-PTZ-2Me-H (ax), O-PTZ-H (eq), O-PTZ-H-1Me (eq) and O-PTZ-H-2Me (eq)) is investigated theoretically. Results reveal that molecules with quasi-equatorial (eq) conformation have superior spin-orbit coupling (SOC) effects and more restricted geometric changes than molecules with quasi-axial (ax) conformation. In particular, the O-PTZ-H-1Me (eq) molecule has a minimum reorganization energy (429.16 meV) due to strong intermolecular interactions, significantly inhibits the non-radiative transition process (3.05×103 s-1), and exhibits a maximum intersystem crossing (ISC) rate (6.02×108 s-1) for efficient triplet exciton generation. In addition, the RTP properties of O-PTZ-H-1Me (eq) are sensitive to hydrostatic pressure, and the quantum efficiency and lifetime are impressively improved by applying high hydrostatic pressure. Because of the small reorganization energy (562.80 meV) and SOC constant (3.98 cm-1) at 0 Gpa, the minimum non-radiative rate (2.64×101 s-1) is achieved, resulting in a 64-fold (44.24

关键词： Hydrostatic pressure

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Spiral Scanning and Self-Supervised image Reconstruction Enable Ultra-Sparse Sampling Multispectral Photoacoustic Tomography

arXiv

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arXiv 2024年

作者： Zhong, Yutian Zhang, Xiaoming Mo, Zongxin Zhang, Shuangyang Chen, Wufan Qi, Li School of Biomedical Engineering Southern Medical University Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangdong Guangzhou China Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangdong Guangzhou China

Multispectral photoacoustic tomography (PAT) is an imaging modality that utilizes the photoacoustic effect to achieve non-invasive and high-contrast imaging of internal tissues. However, the hardware cost and computational demand of a multispectral PAT system consisting of up to thousands of detectors are huge. To address this challenge, we propose an ultra-sparse spiral sampling strategy for multispectral PAT, which we named U3S-PAT. Our strategy employs a sparse ring-shaped transducer that, when switching excitation wavelengths, simultaneously rotates and translates. This creates a spiral scanning pattern with multispectral angle-interlaced sampling. To solve the highly ill-conditioned image reconstruction problem, we propose a self-supervised learning method that is able to introduce structural information shared during spiral scanning. We simulate the proposed U3S-PAT method on a commercial PAT system and conduct in vivo animal experiments to verify its performance. The results show that even with a sparse sampling rate as low as 1/30, our U3S-PAT strategy achieves similar reconstruction and spectral unmixing accuracy as non-spiral dense sampling. Given its ability to dramatically reduce the time required for three-dimensional multispectral scanning, our U3S-PAT strategy has the potential to perform volumetric molecular imaging of dynamic biological activities. Copyright © 2024, The Authors. All rights reserved.

关键词： image reconstruction

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Low-light image enhancement model based on self-calibration illumination and color adjustment

Low-light image enhancement model based on self-calibration ...

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Guiwen Zhang Qishen Li Qiufeng Li Taida Wu School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

to improve the clarity of objects in a dark-light environment, and to facilitate the identification and detection of targets behind. People perceive the color and brightness of a point not only depending on the pixel value of the point but also the absolute light entering the human eye is related to the color and brightness around the point. The self-calibration model we used considers the surrounding information, which avoids the information pollution, such as large regions of dark background information. In this model, we introduce a heterogeneous convolution filter, which makes reasonable use of different parts of the filter. Through this operation, information from multiple different scale spaces can be fused, and the field of view when applying the convolution layer is greatly increased without increasing the hyper-parameters, thus producing a more distinctive feature representation. Then the self-calibration model is combined with the backbone reinforcement network, which can not only retain the information of the original scale space but also efficiently collect the latent space information to guide the feature transformation in the original space. After the different channels of the image are processed separately, the dependency between the channels can be established by using the heterogeneous convolution filter. Finally, the test on the ExDark data set proves that our dark target enhancement effect has been significantly improved.

关键词： Training Pollution image color analysis Convolution Brightness Lighting Information filters

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Speech Stuttering Detection and Removal Using Deep Neural Networks

Speech Stuttering Detection and Removal Using Deep Neural Ne...

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作者： Shaswat Rajput Ruban Nersisson Alex Noel Joseph Raj A.Mary Mekala Olga Frolova Elena Lyakso School of Electrical Engineering Vellore Institute of Technology Key Laboratory of Digital Signal and Image Processing of Guangdong Province Department of Electronic Engineering College of Engineering Shantou University School of Information Technology and Engineering Vellore Institute of Technology St.Petersburg State University

There are more than 70 million people worldwide who suffer from stuttering problems. This will affect the confidence of public speaking in people who suffer from this issue. To solve this problem many people take therapy sessions but the therapy sessions are a temporary solution, as soon as they leave therapy sessions this problem might arise again. This work aims to use state of the art machine learning algorithms that have improved over the past few years to solve this problem. We have used the dataset from UCLASS archives which provide the data for stuttered speech *** format with time-aligned transcriptions. We have tried different algorithms and optimized our model by hyper parameter tuning to maximize the model's accuracy. The algorithm is tested on random speech data with low to heavy stuttering from the same dataset, and it is observed that there is significant reduction in the Word Error Rate(WER) for most of the test cases.

关键词： Speech stuttering Classification Deep learning Word Error Rate Filtering

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Medical image Fusion for High-Level Analysis: A Mutual Enhancement Framework for Unaligned PAT and MRI

arXiv

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arXiv 2024年

作者： Zhong, Yutian He, Jinchuan Liang, Zhichao Zhang, Shuangyang Feng, Qianjin Lu, Lijun Qi, Li School of Biomedical Engineering Southern Medical University Guangdong Guangzhou China Guangdong Provincial Key Laboratory of Medical Image Processing Southern Medical University Guangdong Guangzhou China Guangdong Province Engineering Laboratory for Medical Imaging and Diagnostic Technology Southern Medical University Guangdong Guangzhou China

Photoacoustic tomography (PAT) offers optical contrast, whereas magnetic resonance imaging (MRI) excels in imaging soft tissue and organ anatomy. The fusion of PAT with MRI holds promising application prospects due to their complementary advantages. Existing image fusion have made considerable progress in pre-registered images, yet spatial deformations are difficult to avoid in medical imaging scenarios. More importantly, current algorithms focus on visual quality and statistical metrics, thus overlooking the requirements of high-level tasks. To address these challenges, we propose an unsupervised fusion model, termed PAMRFuse+, which integrates image generation and registration. Specifically, a cross-modal style transfer network is introduced to simplify cross-modal registration to single-modal registration. Subsequently, a multi-level registration network is employed to predict displacement vector fields. Furthermore, a dual-branch feature decomposition fusion network is proposed to address the challenges of cross-modal feature modeling and decomposition by integrating modality-specific and modality-shared features. PAMRFuse+ achieves satisfactory results in registering and fusing unaligned PAT-MRI datasets. Moreover, for the first time, we evaluate the performance of medical image fusion with multi-organ instance segmentation. Extensive experimental demonstrations reveal the advantages of PAMRFuse+ in improving the performance of medical image analysis tasks. Copyright © 2024, The Authors. All rights reserved.

关键词： image fusion

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Pedestrian detection based on multi-modal cooperation

Pedestrian detection based on multi-modal cooperation

引用

IEEE Workshop on Multimedia Signal processing

作者： Yan-ning Zhang Xiao-min Tong Xiu-wei Zhang Jiang-bin Zheng Jun Zhou Si-wei You Institute of Neurosciences Fourth Military Medical University Xi'an China Key Laboratory of Shanxi Province for Speech and Image Processing College of Computer Science Northwestern Polytechnical University Xi'an China School of Astronautics College of Computer Science Northwestern Polytechnical University Xi'an China

ISBN: (纸本)9781424422944;9781424422951

Pedestrian detection plays an important role in automated surveillance system. However, it is challenging to detect pedestrian robustly and accurately in a cluttered environment. In this paper, we propose a new cooperative pedestrian detection method using both colour and thermal image sequences, which is compared with the method using only colour image sequence and that using multi-modal fusion. Experiment results show that our cooperative detection mechanism could get more accurate pedestrian areas, a lower false alarm rate and a higher detection precision. Therefore, it has broad application prospects in the field of industry and military.

关键词： image color analysis image sequences Object detection Information processing Biological information theory Biology Color

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Abundant Semantic Information Module for Object Detection

Abundant Semantic Information Module for Object Detection

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Algorithms, High Performance Computing and Artificial Intelligence (AHPCAI), International Conference on

作者： Taida Wu Qishen Li Qiufeng Li Jun Gao School of Information Engineering Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Jiangxi Province for Image Processing and Pattern Recognition Nanchang Jiangxi China School of Software Nanchang Hangkong University Nanchang Jiangxi China Key Laboratory of Nondestructive Testing (Ministry of Education) Nanchang Hangkong University Nanchang Jiangxi China

Nowadays, with the high-speed iteration of convolution neural network, the efficient object detector emerges one after another. As an important branch of computer vision, object detection aims to detect where and what the object is. However, nowadays, many detector cannot extract abundant semantic information to discriminate the location and size of the objects, resulting in poor performance of the network. In this paper, a new module is proposed, named Abundant Semantic Information Module (ASIM), to enrich and expand the semantic information of the object with more and larger receptive fields. In ASIM, we blend the extracted feature maps to different degrees with different blending factors and fuse them so that all object information is given full attention. Compared to the baseline method, a wealth of experiments show that our module has achieved a significant performance improvement.

关键词： Location awareness Fuses High performance computing Semantics Neural networks Object detection Detectors

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Modulating Excited State Properties of Thermally Activated Delayed Fluorescence Molecules by Hybrid Long-Range and Short-Range Charge Transfer Strategy

SSRN

引用

SSRN 2023年

作者： Zhao, Xin Liu, Huanling Mu, Qingfang Fan, Jianzhong Xu, Yuanyuan Shandong Province Key Laboratory of Medical Physics and Image Processing Technology Institute of Materials and Clean Energy School of Physics and Electronics Shandong Normal University Jinan250014 China School of Science Qilu University of Technology Shandong Academy of Sciences Jinan250353 China

Balancing the rapid radiative decay process and the fast reverse intersystem crossing (RISC) process of thermally activated delayed fluorescence (TADF) molecule remains a great challenge and efficient molecular design strategies are highly desired. Herein, from a theoretical perspective, excited state properties of three reported TADF molecules (1TICz, 1BOICz and 2BOICz) are investigated based on density functional theory (DFT) and time-dependent density functional theory (TD-DFT) calculations coupled with the thermal vibration correlation function (TVCF) method. Results indicate that, by introducing the multi-resonance (MR) acceptor, 1BOICz possesses hybrid long-range and short-range charge transfer features, balanced small energy gap (ΔEST) and large oscillator strength (f) is obtained. Furthermore, by incorporating double equivalent MR acceptors in 2BOICz, largely enhanced f with slightly changed ΔEST is achieved, inner mechanism for remarkable photophysical property is illustrated. Keep this strategy, seven new TADF molecules (2pDBA-bICz-1, 2pDBA-bICz-2, 2OSBA-bICz, 2DQAO-bICz, 2QAO-bICz, 2SQAO-bICz and 2OQAO-bICz) are theoretically designed, detailed physical parameters are analyzed and excited state energy consumption process is studied. Strong electrophilicity on acceptor is determined and the strength of nucleophilic sites on the bridge-phenyl of 2DQAO-bICz, 2QAO-bICz, 2SQAO-bICz and 2OQAO-bICz is increased, this promotes the short-range charge transfer property. In addition, the excitation processes for all studied molecules are dominated by long-range charge transfer from donor to acceptors, and supplemented by the short-range charge transfer on the bridge-phenyl with MR effect. Compromise energy gap and oscillator strength as well as large spin orbit coupling (SOC) constant are obtained for designed molecules. Thus, by regulating the long-range and short-range charge transfer ratios, excited state properties are successfully modulated and new efficient TA

关键词： Energy gap

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A Spatio-Temporal Scale Based Affective Neural Network for Emotion Detection in Conversation

A Spatio-Temporal Scale Based Affective Neural Network for E...

引用

Chinese Automation Congress (CAC)

作者： Jiang Li Xiaoping Wang Shanglin Lei Zhigang Zeng School of Artificial Intelligence and Automation Huazhong University of Science and Technology (HUST) Wuhan China Institute of Artificial Intelligence Huazhong University of Science and Technology (HUST) Wuhan China Key Laboratory of Image Processing and Intelligent Control Ministry of Education Wuhan China Key Laboratory of Brain-inspired Intelligent Systems Hubei Province Wuhan China

Emotion Detection in Conversation (EDC) has been implemented in numerous domains and is under increasing attention. The EDC task is highly challenging due to the need to incorporate both the context of the utterance and the identity of the participant. Some existing EDC approaches employ redundant network structures and inadequately model contextual utterances, resulting in limited performance gains. In this work, we devise a Spatio-Temporal scale based Affective neural Network (STANet) in a simplistic and efficacious manner. STANet consists of two main components, i.e., spatial scale network and temporal scale network, which are applied to extract spatial scale and temporal scale information in the conversation, respectively. Prior to spatio-temporal scale modeling, we encode the identity information of participants to examine its impact on the performance of emotion detection. We conduct substantial comparative experiments and ablation studies on three conversational emotion datasets, and the results demonstrate that our STANet realizes superior performance without taking advantage of external knowledge.

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