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International Conference on image, Vision and Intelligent Systems, ICIVIS 2023

作者： Zhang, Wenbo Sun, Xiaolin Ma, Shiyi Yang, Jinzhu Grzegorzek, Marcin Wang, Yuexi Li, Chen Microscopic Image and Medical Image Analysis Group College of Medicine and Biological Information Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang China Institute of Medical Informatics University of Luebeck Luebeck Germany Department of Knowledge Engineering University of Economics in Katowice Katowice Poland Department of Rehabilitation Shengjing Hospital of China Medical University Shenyang China

ISBN: (纸本)9789819708543

Skin cancer is a type of cancer and has emerged as one of the most prevalent types over the past decade. Early diagnosis and treatment are a key factor in the treatment of skin cancer. The traditional clinical diagnosis method of skin cancer is dermoscopy. On account of the complexity of the skin image, occasional instances of missed diagnosis and misdiagnosis arise, leading to delayed optimal treatment for patients. Two different networks, Inception-V3 and ResNet-50, are put forward in this paper. The dataset is separated into two categories, and the network performance is evaluated using evaluation indicators. Findings indicate that the ResNet50 network attains a higher accuracy rate of 88.83% compared to the Inception-V3 network's 83.17% accuracy. Additionally, the other three indicators of ResNet-50 are also surpass those of Inception-V3. © The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd. 2024.

关键词： Diagnosis

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CAB: Empathetic Dialogue Generation with Cognition, Affection and Behavior 28th

CAB: Empathetic Dialogue Generation with Cognition, Affecti...

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28th International Conference on Database Systems for Advanced Applications, DASFAA 2023

作者： Gao, Pan Han, Donghong Zhou, Rui Zhang, Xuejiao Wang, Zikun School of Computer Science and Engineering Northeastern University Shenyang China Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education Northeastern University Shenyang China Swinburne University of Technology Hawthorn Australia

ISBN: (纸本)9783031306747

Empathy is an important characteristic to be considered when building a more intelligent and humanized dialogue agent. However, existing methods did not fully comprehend empathy as a complex process involving three aspects: cognition, affection and behavior. In this paper, we propose CAB, a novel framework that takes a comprehensive perspective of cognition, affection and behavior to generate empathetic responses. For cognition, we build paths between critical keywords in the dialogue by leveraging external knowledge. This is because keywords in a dialogue are the core of sentences. Building the logic relationship between keywords, which is overlooked by the majority of existing works, can improve the understanding of keywords and contextual logic, thus enhance the cognitive ability. For affection, we capture the emotional dependencies with dual latent variables that contain both interlocutors’ emotions. The reason is that considering both interlocutors’ emotions simultaneously helps to learn the emotional dependencies. For behavior, we use appropriate dialogue acts to guide the dialogue generation to enhance the empathy expression. Extensive experiments demonstrate that our multi-perspective model outperforms the state-of-the-art models. © 2023, The Author(s), under exclusive license to Springer Nature Switzerland AG.

关键词： Knowledge management

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Layph: Making Change Propagation Constraint in Incremental Graph Processing by Layering Graph 39

Layph: Making Change Propagation Constraint in Incremental G...

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39th IEEE International Conference on Data Engineering, ICDE 2023

作者： Yu, Song Gong, Shufeng Zhang, Yanfeng Yu, Wenyuan Yin, Qiang Tian, Chao Tao, Qian Yan, Yongze Yu, Ge Zhou, Jingren Northeastern University China Alibaba Group China Shanghai Jiao Tong University China Chinese Academy of Sciences China Northeastern University Key Laboratory of Intelligent Computing in Medical Image of Ministry of Education China

ISBN: (纸本)9798350322279

Real-world graphs are constantly evolving, which demands updates of the previous analysis results to accommodate graph changes. By using the memoized previous computation state, incremental graph computation can reduce unnecessary recomputation. However, a small change may propagate over the whole graph and lead to large-scale iterative computations. To address this problem, we propose Layph, a two-layered graph framework. The upper layer is a skeleton of the graph which is much smaller than the original graph, and the lower layer has some disjoint subgraphs. Layph limits costly global iterative computations on the original graph to the small graph skeleton and a few subgraphs updated with the input graph changes. In this way, many vertices and edges are not involved in iterative computations, which significantly reduces the computation overhead and improves the performance of incremental graph processing. Our experimental results show that Layph outperforms current state-of-the-art incremental graph systems by 9.08× on average (up to 36.66×) in response time. © 2023 IEEE.

关键词： Iterative methods

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Feature Selection for Microarray Data via Community Detection Fusing Multiple Gene Relation Networks Information

Feature Selection for Microarray Data via Community Detectio...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Zhang, Shoujia Li, Wei Xie, Weidong Wang, Linjie Northeastern University College of Computer Science and Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China

ISBN: (纸本)9781665468190

In recent decades, the rapid development of gene sequencing and computer technology has increased the growth of high-dimensional microarray data. Some machine learning methods have been successfully applied to it to help classify cancer. In most cases, high dimensionality and the small sample size of microarray data restricted the performance of cancer classification. This problem usually issolved bysome feature selection methods. However, most of them neglect the exploitation of relations among genes. This paper proposes a novel feature selection method by fusing multiple gene relation network information based on community detection (MGRCD). The proposed method divides all genes into different communities. Then, the genes most associated with cancer classification are selected from each community. The proposed method satisfies both maximum relevances gene with cancer and minimum redundancy among genes for the selected optimal feature subset. The experiment results show that the proposed gene selection method can effectively improve classification performance. © 2022 IEEE.

关键词： Genes

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Dynamic Prediction of Intraoperative Hypotension Based on Hemodynamic Monitoring Data With a Transformer-Based Deep Learning Model

Dynamic Prediction of Intraoperative Hypotension Based on He...

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2024 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2024

作者： Yang, Kai Ren, Mucheng Xu, Jun Zeng, Xian Nanjing University of Information Science and Technology Jiangsu Key Laboratory of Intelligent Medical Image Computing School of Future Technology Nanjing China

ISBN: (纸本)9798350386226

Timely prediction and intervention for Intraoperative Hypotension (IOH), a prevalent complication associated with general anesthesia, is crucial to prevent severe postoperative outcomes. While existing machine learning methods for IOH prediction have shown promise, they face limitations such as reliance on single data sources and disregard for crucial features like the trend in Arterial Blood Pressure (ABP) waveforms. To address these challenges, this paper proposes a novel multichannel deep learning framework that combines Convolutional Neural Networks (CNN) and Transformers for automatic IOH prediction. The model leverages four types of physiological waveforms, including ABP, ElectroCardioGram (ECG), photoplethysmography (PLE), and Carbon Dioxide (CO2), to capture both local and global information, enhancing information representation. Experimental results on retrospective data of 14,140 adult patients undergoing non-cardiac surgery from VitalDB, a public data repository of vital signs taken during surgeries in 10 operating rooms at Seoul National university Hospital (from January 6, 2005 to March 1, 2014), demonstrate that the proposed model consistently outperforms other methods, achieving superior AUROC values of 0.943, 0.928, and 0.923 at 5, 10, and 15 minutes before the event, respectively. These results demonstrate the powerfulness of multi-modal data source as well as the importance of ABP waveform trends. Additionally, the incorporation of multi-task learning and the attention mechanism validates the effectiveness and superiority of our model, highlighting its potential for proactive clinical interventions and improved postoperative patient outcomes. © 2024 IEEE.

关键词： Blood pressure

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TIDE: a Timing-Deterministic and Efficient Executor for micro-ROS

TIDE: a Timing-Deterministic and Efficient Executor for micr...

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IEEE/ACM Int'l Conference on & Int'l Conference on Cyber, Physical and Social computing (CPSCom) Green computing and Communications (GreenCom)

作者： Zilong Wang Songran Liu Xu Jiang Dong Ji Yi Wang Key Laboratory of Intelligent Computing in Medical Image Northeastern University China

The micro-ROS is widely used to bridge the gap between resource-constrained microcontrollers and larger processors in ROS-based robotic applications. We found that there are some performance issues in micro-ROS because of the unreasonable design of the micro-ROS default RCLC Executor. To improve the timing predictability and run-time efficiency in micro-ROS, we have designed and implemented a new executor called the TIDE Executor, and conducted comprehensive experiments to evaluate the TIDE Executor. The experimental results demonstrate that our TIDE Executor exhibits superior performance compared to the default RCLC Executor.

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Indoor Multi-Anchor Collaborative Localization for Unmanned Systems

Indoor Multi-Anchor Collaborative Localization for Unmanned ...

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2024 IEEE International Conference on Robotics and Biomimetics, ROBIO 2024

作者： Tai, Beijia Wei, Yangjie Zhang, Guangyu He, Yuqing Cui, Siyao Ministry of Education College of Computer Science and Engineering Northeastern University Key Laboratory of Intelligent Computing in Medical Image Wenhua Street 3 Shenyang110819 China Shenyang Institute of Automation Chinese Academy of Science Shenyang China

ISBN: (纸本)9781665481090

Accurate tracking of unmanned devices is essen-tial for higher-level decision-making in collaborative confrontation scenarios. However, the robustness of current positioning methods based on Ultra-Wideband (UWB) is limit, and the positioning error is unstable. This paper presents a collabo-rative localization algorithm specifically designed for indoor unmanned systems, which leverages UWB technology combined with Least Squares (LS) and Extended Kalman Filter (EKF) models to achieve anchor calibration, as well as precise and stable positioning of unmanned devices. First, the positions of anchors is estimated using fixed-point ranging information;Second, the positions of anchors are simultaneously corrected while locating the tags on unmanned devices. The experimental results show that our algorithm achieves positioning accuracy within 10 cm in the three-dimensional space, even with a ranging error of 5 cm. © 2024 IEEE.

关键词： Ultra-wideband (UWB)

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A multi-instance tumor subtype classification method for small PET datasets using RA-DL attention module guided deep feature extraction with radiomics features

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Computers in Biology and Medicine 2024年 174卷 108461-108461页

作者： Diao, Zhaoshuo Jiang, Huiyan Software College Northeastern University Shenyang110819 China Key Laboratory of Intelligent Computing in Medical Image Ministry of Education Northeastern University Shenyang110819 China

Background: Positron emission tomography (PET) is extensively employed for diagnosing and staging various tumors, including liver cancer, lung cancer, and lymphoma. Accurate subtype classification of tumors plays a crucial role in formulating effective treatment plans for patients. Notably, lymphoma comprises subtypes like diffuse large B-cell lymphoma and Hodgkin's lymphoma, while lung cancer encompasses adenocarcinoma, small cell carcinoma, and squamous cell carcinoma. Similarly, liver cancer consists of subtypes such as cholangiocarcinoma and hepatocellular carcinoma. Consequently, the subtype classification of tumors based on PET images holds immense clinical significance. However, in clinical practice, the number of cases available for each subtype is often limited and imbalanced. Therefore, the primary challenge lies in achieving precise subtype classification using a small dataset. Method: This paper presents a novel approach for tumor subtype classification in small datasets using RA-DL (Radiomics-DeepLearning) attention. To address the limited sample size, Support Vector Machines (SVM) is employed as the classifier for tumor subtypes instead of deep learning methods. Emphasizing the importance of texture information in tumor subtype recognition, radiomics features are extracted from the tumor regions during the feature extraction stage. These features are compressed using an autoencoder to reduce redundancy. In addition to radiomics features, deep features are also extracted from the tumors to leverage the feature extraction capabilities of deep learning. In contrast to existing methods, our proposed approach utilizes the RA-DL-Attention mechanism to guide the deep network in extracting complementary deep features that enhance the expressive capacity of the final features while minimizing redundancy. To address the challenges of limited and imbalanced data, our method avoids using classification labels during deep feature extraction and instead incorporates

关键词： Classification (of information)

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A Hybrid Feature Selection Method Based on Binary Differential Evolution and Feature Subset Correlation for Microarray Data∗

A Hybrid Feature Selection Method Based on Binary Differenti...

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2022 IEEE International Conference on Bioinformatics and Biomedicine, BIBM 2022

作者： Xie, Weidong Li, Wei Fang, Yushan Chi, Yuhuan Yu, Kun Northeastern University Computer Science and Engineering Shenyang China Northeastern University Key Laboratory of Intelligent Computing in Medical Image Shenyang China Northeastern University Medicine and Bioinformation Engineering Shenyang China

ISBN: (纸本)9781665468190

Obtaining essential genes from microarray data that can diagnose diseases can be very useful for researchers to understand diseases and develop drugs. However, the high computational cost due to the 'curse of dimensionality' and the high redundancy among features limit the application of evolutionary algorithms to the feature selection problem for high-dimensional data. This paper proposes a two-stage hybrid feature selection method to address this problem. In the first stage, a simple and efficient filtering me thod is used to initially filter redundant features, reduce the feature dimensionality, and reduce the search space of the evolutionary algorithm in the second stage. In the second stage, we propose an improved differential evolution algorithm. We redesign the binary quantization of the differential evolution algorithm for the characteristics of microarray data and improve the algorithm's variation process to balance the algorithm's search efficiency and accuracy. In addition, we define the redundancy of feature subsets and add it to the fitness function to reduce the redundancy of the final feature subsets. The proposed method is compared with classical feature selection methods and advanced hybrid feature selection methods on eight publicly available microarray data, and the effectiveness and advancement of the proposed method are demonstrated. © 2022 IEEE.

关键词： Feature Selection

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An Unsupervised Domain Adaptation Model Based on Multi-Level Joint Alignment for Multi-Modal Cardiac image Segmentation

An Unsupervised Domain Adaptation Model Based on Multi-Level...

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2023 International Conference on Biomimetic Intelligence and Robotics

作者： Li, Jiapeng Lv, Yimou Xu, Lisheng Qi, Lin College of Medicine and Biological Information Engineering Northeastern University Shenyang110169 China Engineering Research Center of Medical Imaging and Intelligent Analysis Ministry of Education Shenyang110169 China Key Laboratory of Medical Image Computing Ministry of Education Northeastern University Shenyang110169 China

Unsupervised Domain Adaptation has greatly boosted the performance of multi-modal medical segmentation when there are only source domain labels and no labels in the target domain. Many previous work relies on Convolutional Neural Networks for distribution or instance alignment, however, the receptive field of CNN makes it overly concerned with texture and local semantic information of images, while losing the global style information and semantic information and lacking relationships between local and global semantics. In order to address these issues, we propose a two-stage, multi-level framework for unsupervised domain adaptation, which consists of an image translation network and a Transformer-based domain adaptation segmentation network, jointly aligning the data distribution in the source and target domains from the image, feature and output level through adversarial learning. Experimental results indicate that our method can achieve satisfactory results and outperform other state-of-the-art medical image segmentation methods. © 2023 The Authors. Published by Elsevier B.V.

关键词： Textures

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