检索结果-内蒙古大学图书馆

TechRxiv 2021年

作者： Ma, Jie Chai, Qi Huang, Jingyue Liu, Jun You, Yang Zheng, Qinghua National Engineering Lab for Big Data Analytics School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China Shaanxi Province Key Laboratory of Satellite and Terrestrial Network Tech. R&D School of Computer Science and Technology Xi’an Jiaotong University Shaanxi Xi’an710049 China The High Performance Computing for Artificial intelligence Lab Department of Computer Science National University of Singapore 117417 Singapore

Textbook Question Answering (TQA) is the task of answering diagram and non-diagram questions given large multimodal contexts consisting of abundant text and diagrams. Deep text understandings and effective learning of diagram semantics are important for this task due to its specificity. In this paper, we propose a Weakly Supervised learning method for TQA (WSTQ), which regards the incompletely accurate results of essential intermediate procedures for this task as supervision to develop Text Matching (TM) and Relation Detection (RD) tasks and then employs the tasks to motivate itself to learn strong text comprehension and excellent diagram semantics respectively. Specifically, we apply the result of text retrieval to build positive as well as negative text pairs. In order to learn deep text understandings, we first pre-train the text understanding module of WSTQ on TM and then fine-tune it on TQA. We build positive as well as negative relation pairs by checking whether there is any overlap between the items/regions detected from diagrams using object detection. The RD task forces our method to learn the relationships between regions, which are crucial to express the diagram semantics. We train WSTQ on RD and TQA simultaneously, i.e., multitask learning, to obtain effective diagram semantics and then improve the TQA performance. Extensive experiments are carried out on CK12-QA and AI2D to verify the effectiveness of WSTQ. Experimental results show that our method achieves significant accuracy improvements of 5.02% and 4.12% on test splits of the above datasets respectively than the current state-of-the-art baseline. We have released our code on https://***/dr-majie/WSTQ. © 2021, CC BY.

关键词： Semantics

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A personalized diagnostic generation framework based on multi-source Heterogeneous data

arXiv

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arXiv 2021年

作者： Wu, Jialun Gao, Zeyu Zhang, Haichuan Zhang, Ruonan Gong, Tieliang Wang, Chunbao Li, Chen School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University ParkPA United States Library of Xi’an Jiaotong University Xi’an China Department of Pathology First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

Personalized diagnoses have not been possible due to sear amount of data pathologists have to bear during the day-today routine. This lead to the current generalized standards that are being continuously updated as new findings are reported. It is noticeable that these effective standards are developed based on a multi-source heterogeneous data, including whole-slide images and pathology and clinical reports. In this study, we propose a framework that combines pathological images and medical reports to generate a personalized diagnosis result for individual patient. We use nuclei-level image feature similarity and content-based deep learning method to search for a personalized group of population with similar pathological characteristics, extract structured prognostic information from descriptive pathology reports of the similar patient population, and assign importance of different prognostic factors to generate a personalized pathological diagnosis result. We use multi-source heterogeneous data from TCGA (The Cancer Genome Atlas) database. The result demonstrate that our framework matches the performance of pathologists in the diagnosis of renal cell carcinoma. This framework is designed to be generic, thus could be applied for other types of cancer. The weights could provide insights to the known prognostic factors and further guide more precise clinical treatment protocols. © 2021, CC BY.

关键词： Diagnosis

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SSM-DTA: Breaking the Barriers of data Scarcity in Drug-Target Affinity Prediction

arXiv

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arXiv 2022年

作者： Pei, Qizhi Wu, Lijun Zhu, Jinhua Xia, Yingce Xie, Shufang Qin, Tao Liu, Haiguang Liu, Tie-Yan Yan, Rui Gaoling School of Artificial Intelligence Renmin University of China No.59 Zhong Guan Cun Avenue Haidian District Beijing100872 China Microsoft Research AI4Science No.5 Dan Ling Street Haidian District Beijing100080 China CAS Key Laboratory of GIPAS EEIS Department University of Science and Technology of China No.96 JinZhai Road Baohe District Anhui Province Hefei230026 China Engineering Research Center of Next-Generation Intelligent Search and Recommendation Ministry of Education China Beijing Key Laboratory of Big Data Management and Analysis Methods China

Accurate prediction of Drug-Target Affinity (DTA) is of vital importance in early-stage drug discovery, facilitating the identification of drugs that can effectively interact with specific targets and regulate their activities. While wet experiments remain the most reliable method, they are time-consuming and resource-intensive, resulting in limited data availability that poses challenges for deep learning approaches. Existing methods have primarily focused on developing techniques based on the available DTA data, without adequately addressing the data scarcity issue. To overcome this challenge, we present the SSM-DTA framework, which incorporates three simple yet highly effective strategies: (1) A multi-task training approach that combines DTA prediction with masked language modeling (MLM) using paired drug-target data. (2) A semi-supervised training method that leverages large-scale unpaired molecules and proteins to enhance drug and target representations. This approach differs from previous methods that only employed molecules or proteins in pre-training. (3) The integration of a lightweight cross-attention module to improve the interaction between drugs and targets, further enhancing prediction accuracy. Through extensive experiments on benchmark datasets such as BindingDB, DAVIS, and KIBA, we demonstrate the superior performance of our framework. Additionally, we conduct case studies on specific drug-target binding activities, virtual screening experiments, drug feature visualizations, and real-world applications, all of which showcase the significant potential of our work. In conclusion, our proposed SSM-DTA framework addresses the data limitation challenge in DTA prediction and yields promising results, paving the way for more efficient and accurate drug discovery processes. Our code is available at https://***/QizhiPei/SSM-DTA. Copyright © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Structure-aware random fourier kernel for graphs 21

Structure-aware random fourier kernel for graphs

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Proceedings of the 35th International Conference on Neural Information Processing Systems

作者： Jinyuan Fang Qiang Zhang Zaiqiao Meng Shangsong Liang School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China Hangzhou Innovation Center Zhejiang University China and College of Computer Science and Technology Zhejiang University China and AZFT Knowledge Engine Lab China School of Computing Science University of Glasgow United Kingdom and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates School of Computer Science and Engineering Sun Yat-sen University China and Guangdong Key Laboratory of Big Data Analysis and Processing Guangzhou China and Mohamed bin Zayed University of Artificial Intelligence United Arab Emirates

ISBN: (纸本)9781713845393

Gaussian Processes (GPs) define distributions over functions and their generalization capabilities depend heavily on the choice of kernels. In this paper, we propose a novel structure-aware random Fourier (SRF) kernel for GPs that brings several benefits when modeling graph-structured data. First, SRF kernel is defined with a spectral distribution based on the Fourier duality given by the Bochner's theorem, transforming the kernel learning problem to a distribution inference problem. Second, SRF kernel admits a random Fourier feature formulation that makes the kernel scalable for optimization. Third, SRF kernel enables to leverage geometric structures by taking subgraphs as inputs. To effectively optimize GPs with SRF kernel, we develop a variational EM algorithm, which alternates between an inference procedure (E-step) and a learning procedure (M-step). Experimental results on five real-world datasets show that our model can achieve state-of-the-art performance in two typical graph learning tasks, i.e., object classification and link prediction.

关键词：

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A Novel L1 Gain Performance Based Multi-robot System Formulation Control Design Method

A Novel L1 Gain Performance Based Multi-robot System Formula...

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第32届中国控制与决策会议

作者： Xiongjun Wu Jialing Zhou Dequan Li Hongbo Zhao National Defense Key Laboratory of Science and Technology on Electromagnetic Scattering The 802 Institute of Shanghai Academy of Space Flight TechnologyThe Eighth Academy of China Aerospace Science and Technology Corporation School of Automation Nanjing University of Science and Technology School of Mathematics and Big Data Anhui University of Science and Technology School of Automation Science and Electrical Engineering Beihang University

The time-varying formations are usually required in practice for multi-agent systems to track the trajectories generated by multiple *** ability to maintain a stable formation property is helpful in terms of facilitating subsequent tasks such as collaborative detection,cooperative reconnaissance and performing other specific task like fetch and transport goods together as a ***,most of existing methods,on the other hand,are fragile to the measurement noises and network ***,it seems that few results are available that considering the internal and external control loop to arrive the consensus of position,the velocity and the attitude *** overcome these limitations,in this paper,we proposed a novel scheme and developed a novel L gain algorithm,where the integrated two loop robot control scheme is endowed with disturbance rejection ability,and it can maintain the formation structure during motion along a pre-determined or online generated geometric path,and to follow a timing law that dominates the rate of advancement of the *** conditions are established to guarantee the stability,the invariant set property,the L gain index,and also the initial tracking error bounding constraints,which are formulated and presented in terms of LMIs/BMIs and can be readily solved to obtain the *** simulations are carried out to validate the effectiveness of the proposed method,and the five robots as well as the three robots formulation cases are detail *** turns out that,in addition to providing a novel perspective of the formulation control problem with disturbance rejection ability,the approach adopted in this paper also paves the way to several extensions in relation to control of multi-agent systems in accordance with swarm intelligence principles,such as the collision avoidance,the delayed multi-agent system control,the incorporation of multiple simultaneous objectives and control design under communi

关键词： Formation Control L1 Gain Multi-agent Systems Disturbance Rejection Cooperative Detection

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A Precision Diagnostic Framework of Renal Cell Carcinoma on Whole-Slide Images using Deep Learning

A Precision Diagnostic Framework of Renal Cell Carcinoma on ...

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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Jialun Wu Ruonan Zhang Tieliang Gong Xinrui Bao Zeyu Gao Haichuan Zhang Chunbao Wang Chen Li School of Computer Science and Technology Xi’an Jiaotong University Xi’an China National Engineering Lab for Big Data Analytics Xi’an Jiaotong University Xi’an China Library of Xi’an Jiaotong University Xi’an China School of Automation Science and Engineering Xi’an Jiaotong University Xi’an China School of Electrical Engineering and Computer Science Pennsylvania State University University Park PA USA the First Affiliated Hospital of Xi’an Jiaotong University Xi’an China

ISBN: (纸本)9781665429825

Diagnostic pathology, which is the basis and gold standard of cancer diagnosis, provides essential information on the prognosis of the disease and vital evidence for clinical treatment. However, pathological diagnosis is subjective, and differences in observation and diagnosis between pathologists are common. This phenomenon is more evident in hospitals with insufficient medical resources. Deep learning (DL) can be used to identify and classify structures in digital pathology. In order to solve the above difficulties, in this work, we propose a DL framework for generating pathological diagnosis by analyzing histopathological images of renal cell carcinoma. A deep neural network is trained on a large high-quality annotated dataset for accurate tumor area detection, subtyping, and grading. The results show that our framework has achieved pathologist-level accuracy in diagnosis, can generate pathology reports with tumor indicators, and provide pathologists with interpretable auxiliary diagnoses

关键词： Deep learning Pathology Visualization Pipelines Neural networks Ontologies Prostate cancer

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Co-training Based on Semi-Supervised Ensemble Classification Approach for Multi-label data Stream

Co-training Based on Semi-Supervised Ensemble Classification...

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IEEE International Conference on big Knowledge (ICBK)

作者： Zhe Chu Peipei Li Xuegang Hu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education AnHui China School of Computer Science and Information Engineering Hefei University of Technology AnHui China

A large amount of data streams in the form of texts and images has been emerging in many real-world applications. These data streams often present the characteristics such as multi-labels, label missing and new class emerging, which makes the existing data stream classification algorithm face the challenges in precision space and time performance. This is because, on the one hand, it is known that data stream classification algorithms are mostly trained on all labeled single-class data, while there are a large amount of unlabeled data and few labeled data due to it is difficult to obtain labels in the real world. On the other hand, many of existing multi-label data stream classification algorithms mostly focused on the classification with all labeled data and without emerging new classes, and there are few semi-supervised methods. Therefore, this paper proposes a semi-supervised ensemble classification algorithm for multi-label data streams based on co-training. Firstly, the algorithm uses the sliding window mechanism to partition the data stream into data chunks. On the former w data chucks, the multi-label semi-supervised classification algorithm COINS based on co-training is used to training a base classifier on each chunk, and then an ensemble model with w COINS classifiers is generated ensemble model to adapt to the environment of data stream with a large number of unlabeled data. Meanwhile, a new class emerging detection mechanism is introduced, and the w+1 data chunk is predicted by the ensemble model to detect whether there is a new class emerging. When a new label is detected, the classifier is retrained on the current data chunk, and the ensemble model is updated. Finally, experimental results on five real data sets show that: as compared with the classical algorithms, the proposed approach can improve the classification accuracy of multi-label data streams with a large number of missing labels and new labels emerging.

关键词： data models Training Streaming media Adaptation models Partitioning algorithms Correlation Knowledge engineering

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Instance-based vision transformer for subtyping of papillary renal cell carcinoma in histopathological image

arXiv

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arXiv 2021年

作者： Gao, Zeyu Hong, Bangyang Zhang, Xianli Li, Yang Jia, Chang Wu, Jialun Wang, Chunbao Meng, Deyu Li, Chen School of Computer Science and Technology Xi'an Jiaotong University Xi'an Shaanxi710049 China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Xi'an Shaanxi710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an710061 China School of Mathematics and Statistics Xi'an Jiaotong University Xi'an Shaanxi710049 China

Histological subtype of papillary (p) renal cell carcinoma (RCC), type 1 vs. type 2, is an essential prognostic factor. The two subtypes of pRCC have a similar pattern, i.e., the papillary architecture, yet some subtle differences, including cellular and cell-layer level patterns. However, the cellular and cell-layer level patterns almost cannot be captured by existing CNN-based models in large-size histopathological images, which brings obstacles to directly applying these models to such a fine-grained classification task. This paper proposes a novel instance-based Vision Transformer (i-ViT) to learn robust representations of histopathological images for the pRCC subtyping task by extracting finer features from instance patches (by cropping around segmented nuclei and assigning predicted grades). The proposed i-ViT takes top-K instances as input and aggregates them for capturing both the cellular and cell-layer level patterns by a position-embedding layer, a gradeembedding layer, and a multi-head multi-layer self-attention module. To evaluate the performance of the proposed framework, experienced pathologists are invited to selected 1162 regions of interest from 171 whole slide images of type 1 and type 2 pRCC. Experimental results show that the proposed method achieves better performance than existing CNN-based models with a significant margin. © 2021, CC BY.

关键词： Formal languages

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BCS-Net: Boundary, Context and Semantic for Automatic COVID-19 Lung Infection Segmentation from CT Images

arXiv

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arXiv 2022年

作者： Cong, Runmin Yang, Haowei Jiang, Qiuping Gao, Wei Li, Haisheng Wang, Cong Zhao, Yao Kwong, Sam The Institute of Information Science Beijing Jiaotong University Beijing100044 China Beijing Key Laboratory of Big Data Technology for Food Safety Beijing Technology and Business University Beijing100048 China The Department of Computer Science City University of Hong Kong Hong Kong The Beijing Key Laboratory of Advanced Information Science and Network Technology Beijing100044 China The School of Information Science and Engineering Ningbo University Ningbo315211 China The School of Electronic and Computer Engineering Peking University Shenzhen Graduate School Shenzhen518055 China Peng Cheng Laboratory Shenzhen518055 China The Distributed and Parallel Software Lab Huawei Technologies Shenzhen518129 China The City University of Hong Kong Shenzhen Research Institute Shenzhen51800 China

The spread of COVID-19 has brought a huge disaster to the world, and the automatic segmentation of infection regions can help doctors to make diagnosis quickly and reduce workload. However, there are several challenges for the accurate and complete segmentation, such as the scattered infection area distribution, complex background noises, and blurred segmentation boundaries. To this end, in this paper, we propose a novel network for automatic COVID-19 lung infection segmentation from CT images, named BCS-Net, which considers the boundary, context, and semantic attributes. The BCS-Net follows an encoder-decoder architecture, and more designs focus on the decoder stage that includes three progressively Boundary-Context-Semantic Reconstruction (BCSR) blocks. In each BCSR block, the attention-guided global context (AGGC) module is designed to learn the most valuable encoder features for decoder by highlighting the important spatial and boundary locations and modeling the global context dependence. Besides, a semantic guidance (SG) unit generates the semantic guidance map to refine the decoder features by aggregating multi-scale high-level features at the intermediate resolution. Extensive experiments demonstrate that our proposed framework outperforms the existing competitors both qualitatively and quantitatively. © 2022, CC BY-NC-SA.

关键词： COVID-19

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Nuclei grading of clear cell renal cell carcinoma in histopathological image by composite high-resolution network

arXiv

引用

arXiv 2021年

作者： Gao, Zeyu Shi, Jiangbo Zhang, Xianli Li, Yang Zhang, Haichuan Wu, Jialun Wang, Chunbao Meng, Deyu Li, Chen School of Computer Science and Technology Xi'an Jiaotong University Shaanxi Xi'an710049 China National Engineering Lab for Big Data Analytics Xi'an Jiaotong University Shaanxi Xi'an710049 China Department of Pathology The First Affiliated Hospital of Xi'an Jiaotong University Xi'an710061 China School of Mathematics and Statistics Xi'an Jiaotong University Shaanxi Xi'an710049 China

The grade of clear cell renal cell carcinoma (ccRCC) is a critical prognostic factor, making ccRCC nuclei grading a crucial task in RCC pathology analysis. Computer-aided nuclei grading aims to improve pathologists' work efficiency while reducing their misdiagnosis rate by automatically identifying the grades of tumor nuclei within histopathological images. Such a task requires precisely segment and accurately classify the nuclei. However, most of the existing nuclei segmentation and classification methods can not handle the inter-class similarity property of nuclei grading, thus can not be directly applied to the ccRCC grading task. In this paper, we propose a Composite High-Resolution Network for ccRCC nuclei grading. Specifically, we propose a segmentation network called W-Net that can separate the clustered nuclei. Then, we recast the fine-grained classification of nuclei to two cross-category classification tasks, based on two high-resolution feature extractors (HRFEs) which are proposed for learning these two tasks. The two HRFEs share the same backbone encoder with W-Net by a composite connection so that meaningful features for the segmentation task can be inherited for the classification task. Last, a head-fusion block is applied to generate the predicted label of each nucleus. Furthermore, we introduce a dataset for ccRCC nuclei grading, containing 1000 image patches with 70945 annotated nuclei. We demonstrate that our proposed method achieves state-of-the-art performance compared to existing methods on this large ccRCC grading dataset. © 2021, CC BY.

关键词： Grading

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