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arXiv 2023年

作者： Zhou, Hong-Yu Yu, Yizhou Wang, Chengdi Zhang, Shu Gao, Yuanxu Pan, Jia Shao, Jun Lu, Guangming Zhang, Kang Li, Weimin Department of Computer Science The University of Hong Kong Pokfulam Hong Kong Department of Respiratory and Critical Care Medicine Med-X Center for Manufacturing Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu610041 China AI Lab Deepwise Healthcare Beijing100080 China Zhuhai International Eye Center Zhuhai People’s Hospital The First Affiliated Hospital of Faculty of Medicine Macau University of Science and Technology University Hospital Guangdong000000 China Department of Big Data and Biomedical Artificial Intelligence College of Future Technology Peking University Beijing100871 China Guangzhou Laboratory Guangzhou510005 China Clinical Translational Research Center West China Hospital Sichuan University Chengdu610041 China Department of Medical Imaging Jinling Hospital Nanjing University School of Medicine Jiangsu Nanjing210093 China

During the diagnostic process, clinicians leverage multimodal information, such as chief complaints, medical images, and laboratory-test results. Deep-learning models for aiding diagnosis have yet to meet this requirement. Here we report a Transformer-based representation-learning model as a clinical diagnostic aid that processes multimodal input in a unified manner. Rather than learning modality-specific features, the model uses embedding layers to convert images and unstructured and structured text into visual tokens and text tokens, and bidirectional blocks with intramodal and intermodal attention to learn a holistic representation of radiographs, the unstructured chief complaint and clinical history, structured clinical information such as laboratory-test results and patient demographic information. The unified model outperformed an image-only model and non-unified multimodal diagnosis models in the identification of pulmonary diseases (by 12% and 9%, respectively) and in the prediction of adverse clinical outcomes in patients with COVID-19 (by 29% and 7%, respectively). Leveraging unified multimodal Transformer-based models may help streamline triage of patients and facilitate the clinical decision process. © 2023, CC BY.

关键词： Diagnosis

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Reverse Perspective Network for Perspective-Aware Object Counting

Reverse Perspective Network for Perspective-Aware Object Cou...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Yifan Yang Guorong Li Zhe Wu Li Su Qingming Huang Nicu Sebe School of Computer Science and Technology UCAS Beijing China Key Lab of Big Data Mining and Knowledge Management UCAS Beijing China Key Lab of Intelligent Information Processing Institute of Computing Technology CAS Beijing China University of Trento Trento Italy

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

One of the critical challenges of object counting is the dramatic scale variations, which is introduced by arbitrary perspectives. We propose a reverse perspective network to solve the scale variations of input images, instead of generating perspective maps to smooth final outputs. The reverse perspective network explicitly evaluates the perspective distortions, and efficiently corrects the distortions by uniformly warping the input images. Then the proposed network delivers images with similar instance scales to the regressor. Thus the regression network doesn't need multi-scale receptive fields to match the various scales. Besides, to further solve the scale problem of more congested areas, we enhance the corresponding regions of ground-truth with the evaluation errors. Then we force the regressor to learn from the augmented ground-truth via an adversarial process. Furthermore, to verify the proposed model, we collected a vehicle counting dataset based on Unmanned Aerial Vehicles (UAVs). The proposed dataset has fierce scale variations. Extensive experimental results on four benchmark datasets show the improvements of our method against the state-of-the-arts.

关键词： Distortion Training Feature extraction Estimation Convolution Kernel Adaptation models

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How generative Artificial Intelligence can transform drug discovery?

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European journal of medicinal chemistry 2025年 295卷 117825页

作者： Ainin Sofia Jusoh Muhammad Akmal Remli Mohd Saberi Mohamad Tristan Cazenave Chin Siok Fong Institute for Artificial Intelligence and Big Data Universiti Malaysia Kelantan Kota Bharu 16100 Kelantan Malaysia Faculty of Data Science and Computing Universiti Malaysia Kelantan Kota Bharu 16100 Kelantan Malaysia. Electronic address: tgaininsofia98@***. Faculty of Data Science and Computing Universiti Malaysia Kelantan Kota Bharu 16100 Kelantan Malaysia. Electronic address: akmal@umk.edu.my. Health Data Science Lab Department of Genetics and Genomics College of Medical and Health Sciences United Arab Emirates University Al Ain 15551 United Arab Emirates Faculty of Engineering and Technology Multimedia University 75450 Melaka Malaysia Department of Biosystems Engineering Faculty of Agricultural Technology Universitas Brawijaya 65145 Malang East Java Indonesia. Electronic address: saberi@uaeu.ac.ae. LAMSADE Université Paris Dauphine - PSL Paris France. Electronic address: cazenave@lamsade.dauphine.fr. UKM Medical Molecular Biology Institute (UMBI) 56000 Kuala Lumpur Malaysia. Electronic address: chinsiokfong@ppukm.ukm.edu.my.

Generative Artificial Intelligence (Generative AI) is transforming drug discovery by enabling advanced analysis of complex biological and chemical data. This review explores key Generative AI models, including Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), flow-based models and Transformer-based models, with Transformers gaining prominence due to the abundance of text-based biological data and the success of language models like ChatGPT. The paper discusses molecular representations, performance evaluation metrics, and current trends in Generative AI-driven drug discovery, such as protein-protein interactions (PPIs), drug-target interactions (DTIs) and de-novo drug design. However, these approaches face significant challenges, including applicability domain issues, lack of interpretability, data scarcity, novelty, scalability, computational resource limitations, and the absence of standardized evaluation metrics. These challenges hinder model performance, complicate decision-making, and limit the generation of novel and viable drug candidates. To address these issues, strategies such as hybrid models, integration of multiomics datasets, explainable AI (XAI) techniques, data augmentation, transfer learning, and cloud-based solutions are proposed. Additionally, a curated list of databases supporting drug discovery research is provided. The review concludes by emphasizing the need for optimized AI models, robust validation methods, interdisciplinary collaboration, and future academic efforts to fully realize the potential of Generative AI in advancing drug discovery.

关键词： database Drug discovery Drug-target interactions Generative artificial Intelligence Molecules representations Performance metrics Protein-protein interactions

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Binary neural networks: A survey

arXiv

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arXiv 2020年

作者： Qin, Haotong Gong, Ruihao Liu, Xianglong Bai, Xiao Song, Jingkuan Sebe, Nicu State Key Lab of Software Development Environment Beihang University Beijing China Beijing Advanced Innovation Center for Big Data-Based Precision Medicine Beihang University Beijing China Center for Future Media School of Computer Science and Engineering University of Electronic Science and Technology of China Chengdu China Department of Information Engineering and Computer Science University of Trento Trento Italy School of Computer Science and Engineering Beijing Advanced Innovation Center for Big Data and Brain Computing Jiangxi Research Institute Beihang University Beijing China

The binary neural network, largely saving the storage and computation, serves as a promising technique for deploying deep models on resource-limited devices. However, the binarization inevitably causes severe information loss, and even worse, its discontinuity brings difficulty to the optimization of the deep network. To address these issues, a variety of algorithms have been proposed, and achieved satisfying progress in recent years. In this paper, we present a comprehensive survey of these algorithms, mainly categorized into the native solutions directly conducting binarization, and the optimized ones using techniques like minimizing the quantization error, improving the network loss function, and reducing the gradient error. We also investigate other practical aspects of binary neural networks such as the hardware-friendly design and the training tricks. Then, we give the evaluation and discussions on different tasks, including image classification, object detection and semantic segmentation. Finally, the challenges that may be faced in future research are prospected. Copyright © 2020, The Authors. All rights reserved.

关键词： Surveys

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Sparse correspondence analysis for contingency tables

arXiv

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arXiv 2020年

作者： Liu, Ruiping Niang, Ndeye Saporta, Gilbert Wang, Huiwen Beijing Information Science & Technology University Beihang University Beijing China CEDRIC Lab CNAM Paris France School of Economics and Management Beijing Advanced Innovation Center for Big Data & Brain Computing Beihang University Beijing China

Since the introduction of the lasso in regression, various sparse methods have been developed in an unsupervised context like sparse principal component analysis (s-PCA), sparse canonical correlation analysis (s-CCA) and sparse singular value decomposition (s-SVD). These sparse methods combine feature selection and dimension reduction. One advantage of s-PCA is to simplify the interpretation of the (pseudo) principal components since each one is expressed as a linear combination of a small number of variables. The disadvantages lie on the one hand in the difficulty of choosing the number of non-zero coefficients in the absence of a well established criterion and on the other hand in the loss of orthogonality for the components and/or the loadings. In this paper we propose sparse variants of correspondence analysis (CA) for large contingency tables like documents-terms matrices used in text mining, together with pPMD, a deflation technique derived from projected deflation in s-PCA. We use the fact that CA is a double weighted PCA (for rows and columns) or a weighted SVD, as well as a canonical correlation analysis of indicator variables. Applying s-CCA or s-SVD allows to sparsify both rows and columns weights. The user may tune the level of sparsity of rows and columns and optimize it according to some criterium, and even decide that no sparsity is needed for rows (or columns) by relaxing one sparsity constraint. The latter is equivalent to apply s-PCA to matrices of row (or column) profiles. © 2020, CC BY.

关键词： Singular value decomposition

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SSMD: Semi-supervised medical image detection with adaptive consistency and heterogeneous perturbation

arXiv

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arXiv 2021年

作者： Zhou, Hong-Yu Wang, Chengdi Lic, Haofeng Wang, Gang Zhang, Shu Lia, Weimin Yu, Yizhou Department of Respiratory and Critical Care Medicine National Clinical Research Center for Geriatrics Frontiers Science Center for Disease-related Molecular Network West China Hospital Sichuan University Chengdu610041 China Department of Computer Science University of Hong Kong Pokfulam Hong Kong Shenzhen Research Institute of Big Data Chinese University of Hong Kong Shenzhen518000 Hong Kong AI Lab Deepwise Healthcare Beijing100080 China

Semi-Supervised classification and segmentation methods have been widely investigated in medical image analysis. Both approaches can improve the performance of fully-supervised methods with additional unlabeled data. However, as a fundamental task, semi-supervised object detection has not gained enough attention in the field of medical image analysis. In this paper, we propose a novel Semi-Supervised Medical image Detector (SSMD). The motivation behind SSMD is to provide free yet effective supervision for unlabeled data, by regularizing the predictions at each position to be consistent. To achieve the above idea, we develop a novel adaptive consistency cost function to regularize different components in the predictions. Moreover, we introduce heterogeneous perturbation strategies that work in both feature space and image space, so that the proposed detector is promising to produce powerful image representations and robust predictions. Extensive experimental results show that the proposed SSMD achieves the state-of-the-art performance at a wide range of settings. We also demonstrate the strength of each proposed module with comprehensive ablation studies. © 2021, CC BY.

关键词： Supervised learning

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HI-Net: Hyperdense inception 3D UNet for brain tumor segmentation

arXiv

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arXiv 2020年

作者： Qamar, Saqib Ahmad, Parvez Shen, Linlin Computer Vision Institute School of Computer Science and Software Engineering Shenzhen University China National Engineering Research Center for Big Data Technology and System Services Computing Technology and System Lab Cluster and Grid Computing Lab School of Computer Science and Technology Huazhong University of Science and Technology Wuhan430074 China

The brain tumor segmentation task aims to classify tissue into the whole tumor (WT), tumor core (TC) and enhancing tumor (ET) classes using multimodel MRI images. Quantitative analysis of brain tumors is critical for clinical decision making. While manual segmentation is tedious, time-consuming, and subjective, this task is at the same time very challenging to automatic segmentation methods. Thanks to the powerful learning ability, convolutional neural networks (CNNs), mainly fully convolutional networks, have shown promising brain tumor segmentation. This paper further boosts the performance of brain tumor segmentation by proposing hyperdense inception 3D UNet (HI-Net), which captures multi-scale information by stacking factorization of 3D weighted convolutional layers in the residual inception block. We use hyper dense connections among factorized convolutional layers to extract more contexual information, with the help of features reusability. We use a dice loss function to cope with class imbalances. We validate the proposed architecture on the multi-modal brain tumor segmentation challenges (BRATS) 2020 testing dataset. Preliminary results on the BRATS 2020 testing set show that achieved by our proposed approach, the dice (DSC) scores of ET, WT, and TC are 0.79457, 0.87494, and 0.83712, respectively. © 2020, CC BY.

关键词： Convolution

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On-edge multi-task transfer learning: Model and practice with data-driven task allocation

arXiv

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arXiv 2021年

作者： Zheng, Zimu Chen, Qiong Hu, Chuang Wang, Dan Liu, Fangming The National Engineering Research Center for Big Data Technology and System Key Laboratory of Services Computing Technology and System Ministry of Education School of Computer Science and Technology Huazhong University of Science and Technology Wuhan China The Edge Cloud Innovation Lab. Technical Innovation Department Cloud BU Huawei Technologies Co. Ltd. Shenzhen China The Department of Computing Hong Kong Polytechnic University Kowloon Hong Kong Hong Kong

On edge devices, data scarcity occurs as a common problem where transfer learning serves as a widely-suggested remedy. Nevertheless, transfer learning imposes heavy computation burden to the resource-constrained edge devices. Existing task allocation works usually assume all submitted tasks are equally important, leading to inefficient resource allocation at a task level when directly applied in Multi-task Transfer Learning (MTL). To address these issues, we first reveal that it is crucial to measure the impact of tasks on overall decision performance improvement and quantify task importance. We then show that task allocation with task importance for MTL (TATIM) is a variant of NP-complete Knapsack problem, where the complicated computation to solve this problem needs to be conducted repeatedly under varying contexts. To solve TATIM with high computational efficiency, we propose a data-driven Cooperative Task Allocation (DCTA) approach. Finally, we evaluate the performance of DCTA by not only a trace-driven simulation, but also a new comprehensive real-world AIOps case study which bridges model and practice via a new architecture and main components design within AIOps system. Extensive experiments show that our DCTA reduces 3.24 times of processing time, and saves 48.4% energy consumption compared with the state-of-the-art when solving TATIM. Copyright © 2021, The Authors. All rights reserved.

关键词： Energy utilization

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From local to national metabolism:a review and a scale-up framework

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Ecosystem Health and Sustainability 2020年第1期6卷 334-352,333页

作者： Ningyu Yan Gengyuan Liu Maddalena Ripa Ning Wang Hongmei Zheng Francesco Gonella State Key Joint Laboratory of Environment Simulation and Pollution Control School of EnvironmentBeijing Normal UniversityBeijingChina Beijing Engineering Research Center for Watershed Environmental Restoration&Integrated Ecological Regulation BeijingChina Institut De Ciencia I Tecnologia Ambientals Universitat Autonoma De BarcelonaBellaterraSpain Beijing Key Lab of Green Development Decision Based on Big Data Beijing Information Science and Technology UniversityChina Department of Resource Management Tangshan Normal UniversityTangshanChina Department of Molecular Sciences and Nanosystems UniversitàCa’Foscari VeneziaVeniceItaly

Research background:Countries are likely the most important subjects involved in the environmental control and response to global environmental issues,while the majority of the related metabolic analyses are focused on the metabolism at the city ***:Analyzing why and how to scale up the metabolic study from city to ***:Combining quantitative analysis with a literature review socio-metabolic research,since it is an effective method to study resource and environmental issues and has been applied at different ***:1)A single city or a smaller area is hardly self-sufficient,and its sustainability and resilience needs the support of the surrounding environment;2)At the country scale,systems exhibit a higher level of self-organization and a corresponding higher level of complexity,addressing the need for applying the metabolic theory at the national scale;3)The emergy analysis methods show its advantages in study metabolic processes for national metabolism;4)Input-output analysis plays an important role in and region ***:The interactions among and within the scales are nested,as well as the goals and ***,socio-metabolic research on scales differ in their priority goals,and the methods adopted must be targeted.

关键词： National metabolism socio-metabolic research sustainability scale-up

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Revealing secrets in SPARQL session level

arXiv

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arXiv 2020年

作者： Zhang, Xinyue Wang, Meng Saleem, Muhammad Ngomo, Axel-Cyrille Ngonga Qi, Guilin Wang, Haofen School of Computer Science and Engineering Southeast University Nanjing China Ministry of Education Nanjing China AKSW Leipzig University Leipzig Germany University of Paderborn Paderborn Germany Intelligent Big Data Visualization Lab Tongji University Shanghai China

Based on Semantic Web technologies, knowledge graphs help users to discover information of interest by using live SPARQL services. Answer-seekers often examine intermediate results iteratively and modify SPARQL queries repeatedly in a search session. In this context, understanding user behaviors is critical for effective intention prediction and query optimization. However, these behaviors have not yet been researched systematically at the SPARQL session level. This paper reveals secrets of session-level user search behaviors by conducting a comprehensive investigation over massive real-world SPARQL query logs. In particular, we thoroughly assess query changes made by users w.r.t. structural and data-driven features of SPARQL queries. To illustrate the potentiality of our findings, we employ an application example of how to use our findings, which might be valuable to devise efficient SPARQL caching, auto-completion, query suggestion, approximation, and relaxation techniques in the future. Copyright © 2020, The Authors. All rights reserved.

关键词： Query processing

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