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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chuxue Cao Yiming He Yuzhen Chen Chunli Song Hao Ling Renchu Guan Xiaoyue Feng The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Software Jilin University Changchun China The Second Hospital of Jilin University Changchun China The Key Laboratory for Symbolic Computation and Knowledge Engineering of the Ministry of Education College of Computer Science and Technology Jilin University Changchun China

ISBN: (纸本)9781665429825

Medical scene graph contributes to cognitive tasks such as question answering. An automatic medical scene graph generator can annotate medical images with scene graphs conveniently. An end-to-end model is proposed, which contains an automatic medical scene graph generator based on medical images and surgery records. The novel model can extract symptom information of the images from surgery records via name entity recognition then convert it into scene graphs. The content and format of the scene graph are pre-defined according to the features of the images. A medical scene graph (MSG) dataset is also produced. The dataset contains 116 scene graphs where each graph includes 6 to 30 entities. Experiments show that our model performs well on making scene graphs of CT coronary angiography. Significantly, the scene graphs have the type, state, location, and index of arteries. To verify the practicability of the MSG dataset, We construct an intelligent model that can generate key diagnostic results. In addition, our medical scene graph generator can be applied to other medical areas to annotate different kinds of medical images intelligently. And the MSG dataset can directly be used in other cognitive visual tasks.

关键词： Visualization Computed tomography Angiography Surgery Medical services Generators Task analysis

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Variational Wasserstein Barycenters with c-Cyclical Monotonicity Regularization

arXiv

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arXiv 2021年

作者： Chi, Jinjin Yang, Zhiyao Li, Ximing Ouyang, Jihong Guan, Renchu College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education China

Wasserstein barycenter, built on the theory of Optimal Transport (OT), provides a powerful framework to aggregate probability distributions, and it has increasingly attracted great attention within the machine learning community. However, it is often intractable to precisely compute, especially for high dimensional and continuous settings. To alleviate this problem, we develop a novel regularization by using the fact that c-cyclical monotonicity is often necessary and sufficient conditions for optimality in OT problems, and incorporate it into the dual formulation of Wasserstein barycenters. For efficient computations, we adopt a variational distribution as the approximation of the true continuous barycenter, so as to frame the Wasserstein barycenters problem as an optimization problem with respect to variational parameters. Upon those ideas, we propose a novel end-to-end continuous approximation method, namely Variational Wasserstein Barycenters with c-Cyclical Monotonicity Regularization (VWB-CMR), given sample access to the input distributions. We show theoretical convergence analysis and demonstrate the superior performance of VWB-CMR on synthetic data and real applications of subset posterior aggregation. Copyright © 2021, The Authors. All rights reserved.

关键词： Probability distributions

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S3map: Semi-Supervised Aspect-Based Sentiment Analysis with Masked Aspect Prediction

SSRN

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SSRN 2022年

作者： Yang, Zhiyao Wang, Bing Li, Ximing Wang, Wenting Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education Jilin University China Department of Computer Science University of Texas Dallas United States

Aspect-Based Sentiment Analysis (ABSA) refers to a fine-grained task of detecting the sentiment polarities of sentences at the aspect level. To resolve this task, training samples of ABSA must be annotated with aspect words and the corresponding sentiment polarities. However, collecting such fine-grained training samples is expensive and time consuming. Therefore the available ABSA training samples are often scarce. To break the data scarcity challenge of ABSA, we investigate Semi-supervised Aspect-Based Sentiment Analysis (SemiABSA), which trains ABSA models by using a limited amount of expensive labeled sentences and more unlabeled-yet-cheaper ones. We propose a novel SemiABSA framework, namely Semi-supervised aspect-based Sentiment analysis with Masked aspect prediction (S3MAP), built on the self-training paradigm. For unlabeled sentences, we form pseudo-aspect words and pseudo-sentiment polarities and improve model training. Specifically, a BERT-Encoder-based Masked Aspect Prediction (MAP) task achieves the pseudo-aspect words generation. Based on S3MAP, we thoroughly investigate the potential of SemiABSA from various perspectives. Empirical results show that S3MAP can consistently improve the performance by leveraging unlabeled sentences even with those from different domains. © 2022, The Authors. All rights reserved.

关键词： Forecasting

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Research on the Task Scheduling System for Agricultural Plant Protection UAV 19

Research on the Task Scheduling System for Agricultural Plan...

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Proceedings of the 2019 2nd International Conference on Robot Systems and Applications

作者： Sun Fengjie Wang Xianchang Zhang Rui Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering Ministry of Education China Jilin University Key Laboratory of Symbolic Computing and Knowledge Engineering Ministry of Education Chengdu Kestrel Artificial Intelligence Institute China

ISBN: (纸本)9781450365130

Unmanned Aerial Vehicle (UAV) can greatly reduce manpower in agricultural plant protection such as watering, sowing, pesticide spraying. It helps in creating an autonomous manufacturing sys- tem by executing tasks with less human intervention in time-efficient manner. Consequently, a scheduler is one essential component to be focused on; yet to the best of our knowledge, there are few re- search on practical UAV scheduling. This work proposes an algorithm to schedule the UAV actions in practice such as pesticide spraying, flying and charging. During the execution of the algorithm, the tasks are put into a queue then each task is assigned to the UAV that can complete it in the shortest amount of time, and then the UAVs perform tasks in the order in which they are as- signed. This proposed method is implemented into a scheduler and tested on datasets generated based on a real flight demonstration. Performance evaluation of the scheduler is discussed in details.

关键词： task Assignment UAV swarm scheduling

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Reinforced iterative knowledge distillation for cross-lingual named entity recognition

arXiv

引用

arXiv 2021年

作者： Liang, Shining Gong, Ming Pei, Jian Shou, Linjun Zuo, Wanli Zuo, Xianglin Jiang, Daxin College of Computer Science and Technology Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education STCA NLP Group Microsoft School of Computing Science Simon Fraser University

Named entity recognition (NER) is a fundamental component in many applications, such as Web Search and Voice Assistants. Although deep neural networks greatly improve the performance of NER, due to the requirement of large amounts of training data, deep neural networks can hardly scale out to many languages in an industry setting. To tackle this challenge, cross-lingual NER transfers knowledge from a rich-resource language to languages with low resources through pre-trained multilingual language models. Instead of using training data in target languages, cross-lingual NER has to rely on only training data in source languages, and optionally adds the translated training data derived from source languages. However, the existing cross-lingual NER methods do not make good use of rich unlabeled data in target languages, which is relatively easy to collect in industry applications. To address the opportunities and challenges, in this paper we describe our novel practice in Microsoft to leverage such large amounts of unlabeled data in target languages in real production settings. To effectively extract weak supervision signals from the unlabeled data, we develop a novel approach based on the ideas of semi-supervised learning and reinforcement learning. The empirical study on three benchmark data sets verifies that our approach establishes the new state-of-the-art performance with clear edges. Now, the NER techniques reported in this paper are on their way to become a fundamental component for Web ranking, Entity Pane, Answers Triggering, and Question Answering in the Microsoft Bing search engine. Moreover, our techniques will also serve as part of the Spoken Language Understanding module for a commercial voice assistant. We plan to open source the code of the prototype framework after deployment. © 2021, CC BY-NC-SA.

关键词： Deep neural networks

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LarTap: A Luminance-aware Framework with Text-correlation Priors for Multi-Exposure Image Fusion

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IEEE Transactions on Circuits and Systems for Video Technology 2025年

作者： Wang, Enlong Li, Jiawei Yan, Tiantian Lei, Jia Zhou, Shihua Wang, Bin Liu, Jinyuan Kasabov, Nikola K. Dalian University Key Laboratory of Advanced Design and Intelligent Computing Ministry of Education School of Software Engineering Dalian116622 China University of the Science and Technology Beijing School of Computer and Communication Engineering Beijing100083 China Xi’an Jiaotong University School of Software Engineering Shaanxi Xi’an710049 China Dalian Univer sity of Technology School of Mechanical Engineering Dalian116024 China Auckland University of Technology Knowledge Engineering and Discovery Research Institute Auckland1061 New Zealand

Conventional imaging devices often struggle to produce high-dynamic-range (HDR) images that accurately represent natural scenes. To overcome this limitation, multi-exposure image fusion (MEF) techniques have been introduced as a viable solution. Existing MEF approaches aim to enhance performance by optimizing or searching architectures. However, they face challenges in precise feature extraction and scene reconstruction, leading to distortion in the fused images. Additionally, most methods do not adequately address luminance variations across different image regions, which may result in the loss of essential details. To address these challenges, we present a novel luminance-aware MEF framework that integrates text-correlation priors (LarTap). By embedding textual information into fusion process, the proposed framework enhances content extraction and comprehension. Specifically, it consist of two key components: the text-image correlation network (N1) and the multi-exposure fusion network (N2). First, N1 performs correlation training to achieve a holistic alignment between text and image pairs. Its iterative vision encoders (VEs) generate text-correlated prior knowledge to facilitate the fusion process in N2. Second, N2 leverages these priors for scene reconstruction and dynamically adjusts luminance based on comparative perception. Extensive experiments on multiple datasets demonstrate that LarTap outperforms state-of-the-art methods. © 1991-2012 IEEE.

关键词： Image correlation

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Automatic Life Event Tree Generation for Older Adults 1

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24th International Conference on Human-Computer Interaction, HCII 2022

作者： Gui, Fang Wu, Xi Hu, Min Yang, Jiaoyun School of Computer Science and Information Engineering Hefei University of Technology Hefei China Key Laboratory of Knowledge Engineering with Big Data of Ministry of Education Hefei University of Technology Hefei China National Smart Eldercare International S&T Cooperation Base Hefei University of Technology Hefei China Laboratory of Affective Computing and Advanced Intelligent Machine Hefei University of Technology Hefei China Intelligent Interconnected Systems Laboratory of Anhui Province Hefei University of Technology Hefei China

ISBN: (数字)9783031179020

ISBN: (纸本)9783031179013

Studies have shown that learning personal stories could help provide individualized eldercare services. However, personal stories are often disordered because of the scattered collection, including informal interviews or daily interactions, which brings difficulties in acquiring valuable information quickly. One solution to this problem is to extract events from personal stories and automatically organize them in chronological order. Events extracted by current methods from social media or news corpus are mainly organized in a linear structure. These works usually focus on the event time and ignore the consistency of event contents when organizing events. This paper aims to organize events into a tree structure based on an event network, with stem nodes representing key event topics and branch nodes representing detailed events. Social workers or caregivers can clarify the life experience of the older adults quickly through the event tree and have a preliminary understanding of them. The experiments show that the event tree generated by our method has a better performance in consistency than current event organization methods. A survey study shows that our method achieves the highest logical coherence for the event tree branches compared with other algorithms. © 2022, Springer Nature Switzerland AG.

关键词： Trees (mathematics)

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Self-Ensembling Semi-Supervised Model for Bone X-ray Images Landmark Detection

Self-Ensembling Semi-Supervised Model for Bone X-ray Images ...

引用

2021 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2021

作者： Bai, Tian Liu, Shenyao Wang, Yuzhao Wang, Yu Dong, Dong Jilin University College of Computer Science and Technology Changchun China Jilin University Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Changchun China College of Software Jilin University Changchun China Department of Radiology First Hospital of Jilin University Changchun China

ISBN: (纸本)9781665401265

Bone image landmark detection plays a vital role in orthopedic diseases diagnosis and analysis. As a promising approach, the data-driven convolutional neural network has been widely applied in landmarks detection. However, the model's performance relies heavily on numerous high-quality labeled data, which are difficult to access. A practical solution for this issue in bone images landmark detection has not been provided, so we propose a framework of semi-supervised bone images landmark detection encouraged by the recent success of the self-ensembling method. The student and teacher networks share the same structure in our framework, where unlabeled data are utilized by enforcing the prediction consistency of two networks under different perturbations to enhance the network's performance. The two networks use a mutual learning approach to improve the efficiency of using unlabeled data. In addition, we also propose a transformative perturbation scheme to prevent model overfitting during training and enhance the generalization capability of the model on unseen data. Experimental results on an in-house hip dataset collected from the actual process of clinical diagnosis and a publicly available dataset demonstrate that our framework has excellent performance compared with other semi-supervised methods. © 2021 IEEE.

关键词： Convolutional neural networks

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Learning with Partial Labels from Semi-supervised Perspective

arXiv

引用

arXiv 2022年

作者： Li, Ximing Jiang, Yuanzhi Li, Changchun Wang, Yiyuan Ouyang, Jihong College of Computer Science and Technology Jilin University China Key Laboratory of Symbolic Computation and Knowledge Engineering of MOE Jilin University China College of Information Science and Technology Northeast Normal University China Key Laboratory of Applied Statistics of MOE Northeast Normal University China

Partial Label (PL) learning refers to the task of learning from the partially labeled data, where each training instance is ambiguously equipped with a set of candidate labels but only one is valid. Advances in the recent deep PL learning literature have shown that the deep learning paradigms, e.g., self-training, contrastive learning, or class activate values, can achieve promising performance. Inspired by the impressive success of deep Semi-Supervised (SS) learning, we transform the PL learning problem into the SS learning problem, and propose a novel PL learning method, namely Partial Label learning with Semi-supervised Perspective (PLSP). Specifically, we first form the pseudo-labeled dataset by selecting a small number of reliable pseudo-labeled instances with high-confidence prediction scores and treating the remaining instances as pseudo-unlabeled ones. Then we design a SS learning objective, consisting of a supervised loss for pseudo-labeled instances and a semantic consistency regularization for pseudo-unlabeled instances. We further introduce a complementary regularization for those non-candidate labels to constrain the model predictions on them to be as small as possible. Empirical results demonstrate that PLSP significantly outperforms the existing PL baseline methods, especially on high ambiguity levels. Code available: https://***/changchunli/PLSP. Copyright © 2022, The Authors. All rights reserved.

关键词： Deep learning

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Machine learning in diagnosis of coronary artery disease

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Chinese Medical Journal 2021年第4期134卷 401-403页

作者： Hao Ling Zi-Yuan Guo Un-Lin Tan Ren-Chu Guan Jing-Bo Chen Chun-Li Department of Cardiology the Second Hospital of Jilin UniversityChangchunJilin 130012China Key Laboratory of Symbolic Computation and Knowledge Engineering of Ministry of Education College of Computer Science and TechnologyJilin UniversityChangchunJilin 130012China

Cardiovascular diseases continue to be the foremost cause of death among both males and females worldwide.[1]Coronary artery disease(CAD)represents a major subtype of cardiovascular *** evidence has highlighted a worldwide increase in incidence of CAD clinical manifestations regardless of geographical region.[2]Despite a steady decline in CAD mortality in western countries over the past few decades,approximately one-third of the population suffering from CAD will succumb to the *** and accurate diagnostic decisions are crucial for patients with CAD and aid in guidance for effective treatment.

关键词： CAD diagnosis continue

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