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IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

作者： Chen Zhao Tian Bai Tongjia Chu Feng Wei Fa Zhang College of Computer Science and Technology Jilin University Changchun China Key Laboratory of Symbolic Computation and Knowledge Engineering Ministry of Education Jilin University Changchun China Hepatobiliary and Pancreas Department General Surgery Center First Hospital of Jilin University Changchun China School of Medical Technology Beijing Institute of Technology Beijing China

ISBN: (数字)9781665468190

ISBN: (纸本)9781665468206

Microvascular invasion (MVI) in hepatocellular carcinoma (HCC) is of great guiding significance for the formulating treatment strategies and accessing the prognosis before the surgery. However, in traditional medicine, the gold standard for the diagnosis of MVI is obtained by examining pathological images which can only be obtained by sampling and sectioning tumors after surgery. At this time, MVI results have lost the timeliness of guiding tumor resection surgery. In order to solve this problem, existing studies began to use deep learning-based methods for preoperative prediction of MVI using non-invasive imaging. Most of these methods adopt the fusion methods of multi-sequence images to predict MVI, but fail to make full use of the characteristics of multiply sequences as prior knowledge to combine into the model, resulting in no further improvement of prediction performance. So we propose a multi-sequence image difference and correlation deep learning model. The model can extract the difference and correlation information between sequences from different scales and combine them into the model. To validate proposed model, we collected a data set consists of 120 HCC patients, including 50 MVI-positive patients. Compared with existing studies, our method has greatly improved in all evaluation metrics.

关键词： Deep learning Pathology Correlation Biological system modeling Surgery Predictive models Data mining

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Preface

Lecture Notes in Networks and Systems

引用

Lecture Notes in Networks and Systems 2023年 579卷 v页

作者： Nagar, Atulya K. Jat, Dharm Singh Mishra, Durgesh Kumar Joshi, Amit School of Mathematics Computer Science and Engineering Liverpool Hope University Liverpool United Kingdom Namibia University of Science and Technology Windhoek Namibia Department of Computer Science and Engineering Sri Aurobindo Institute of Technology Madhya Pradesh Indore India Global Knowledge Research Foundation Gujarat Ahmedabad India

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Medical entity linking using triplet network

arXiv

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arXiv 2020年

作者： Mondal, Ishani Purkayastha, Sukannya Sarkar, Sudeshna Goyal, Pawan Pillai, Jitesh K. Bhattacharyya, Amitava Gattu, Mahanandeeshwar Department of Computer Science and Engineering IIT Kharagpur India Excelra Knowledge Solutions Pvt Ltd Hyderabad India

Entity linking (or Normalization) is an essential task in text mining that maps the entity mentions in the medical text to standard entities in a given knowledge Base (KB). This task is of great importance in the medical domain. It can also be used for merging different medical and clinical ontologies. In this paper, we center around the problem of disease linking or normalization. This task is executed in two phases: candidate generation and candidate scoring. In this paper, we present an approach to rank the candidate knowledge Base entries based on their similarity with disease mention. We make use of the Triplet Network for candidate ranking. While the existing methods have used carefully generated sieves and external resources for candidate generation, we introduce a robust and portable candidate generation scheme that does not make use of the hand-crafted rules. Experimental results on the standard benchmark NCBI disease dataset demonstrate that our system outperforms the prior methods by a significant margin. © 2020, CC BY.

关键词： knowledge based systems

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Parts2Words: Learning Joint Embedding of Point Clouds and Texts by Bidirectional Matching between Parts and Words

arXiv

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arXiv 2021年

作者： Tang, Chuan Yang, Xi Wu, Bojian Han, Zhizhong Chang, Yi School of Artificial Intelligence Jilin University China Zhejiang University China The Department of Computer Science Wayne State University United States Engineering Research Center of Knowledge-Driven Human-Machine Intelligence MoE China

Shape-Text matching is an important task of high-level shape understanding. Current methods mainly represent a 3D shape as multiple 2D rendered views, which obviously can not be understood well due to the structural ambiguity caused by self-occlusion in the limited number of views. To resolve this issue, we directly represent 3D shapes as point clouds, and propose to learn joint embedding of point clouds and texts by bidirectional matching between parts from shapes and words from texts. Specifically, we first segment the point clouds into parts, and then leverage optimal transport method to match parts and words in an optimized feature space, where each part is represented by aggregating features of all points within it and each word is abstracted by its contextual information. We optimize the feature space in order to enlarge the similarities between the paired training samples, while simultaneously maximizing the margin between the unpaired ones. Experiments demonstrate that our method achieves a significant improvement in accuracy over the SOTAs on multi-modal retrieval tasks under the Text2Shape dataset. Codes are available at here. Copyright © 2021, The Authors. All rights reserved.

关键词： Embeddings

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Size-fixed group discovery via multi-constrained graph pattern matching

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Information sciences 2025年 690卷

作者： Guliu Liu Lei Li Guanfeng Liu Xindong Wu Key Laboratory of Knowledge Engineering with Big Data (Hefei University of Technology) Ministry of Education 230601 Hefei China School of Computer Science and Information Engineering Hefei University of Technology 230601 Hefei China Department of Computing Macquarie University Sydney 2122 Australia

Multi-Constrained Graph Pattern Matching (MC-GPM) aims to match a pattern graph with multiple attribute constraints on its nodes and edges, and has garnered significant interest in various fields, including social-based e-commerce and trust-based group discovery. However, the existing MC-GPM methods do not consider situations where the number of each node in the pattern graph needs to be fixed, such as finding experts group with expert quantities and relations specified. In this paper, a Multi-Constrained Strong Simulation with the Fixed Number of Nodes (MCSS-FNN) matching model is proposed, and then a Trust-oriented Optimal Multi-constrained Path (TOMP) matching algorithm is designed for solving it. Additionally, two heuristic optimization strategies are designed, one for combinatorial testing and the other for edge matching, to enhance the efficiency of the TOMP algorithm. Empirical experiments are conducted on four real social network datasets, and the results demonstrate the effectiveness and efficiency of the proposed algorithm and optimization strategies.

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Superpixel and Density Based Region Segmentation Algorithm for Lung Nodule Detection

Superpixel and Density Based Region Segmentation Algorithm f...

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IEEE Calcutta Conference (CALCON)

作者： Amitava Halder Saptarshi Chatterjee Debangshu Dey Computer Science and Engineering Department Supreme Knowledge Foundation Group of Institutions Hooghly India Electrical Engineering Department Jadavpur University Kolkata India

ISBN: (数字)9781728142838

ISBN: (纸本)9781728153629

Lung nodule detection and segmentation plays an important role in early cancer diagnosis. It is a challenging task owing to the shape and intensity variations of a lung nodule. This paper reports an efficient nodule detection framework in High- Resolution Computed Tomography (HRCT) images. Here, an automated computer-aided lung nodule detection scheme is proposed, combining the concept of superpixel generation and density-based region segmentation algorithm, Superpixel Density-Based Region segmentation (SPDBR). A set of morphological features are extracted from each of the extracted nodule regions. The nodule candidate regions have been classified into the nodule and non-nodule decision using a nonlinear support vector machine (SVM) classifier with an average detection accuracy of 84.75% with 82.86% sensitivity and 86.62% specificity.

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Biomedical relation classification by single and multiple source domain adaptation 10

Biomedical relation classification by single and multiple so...

引用

10th International Workshop on Health Text Mining and Information Analysis, LOUHI@EMNLP 2019

作者： Chakraborty, Sinchani Sarkar, Sudeshna Goyal, Pawan Gattu, Mahanandeeshwar Department of Computer Science and Engineering IIT Kharagpur India Excelra Knowledge Solutions Pvt Ltd Hyderabad India

ISBN: (纸本)9781950737772

Relation classification is crucial for inferring semantic relatedness between entities in a piece of text. These systems can be trained given labelled data. However, relation classification is very domain-specific and it takes a lot of effort to label data for a new domain. In this paper, we explore domain adaptation techniques for this task. While past works have focused on single source domain adaptation for bio-medical relation classification, we classify relations in an unlabeled target domain by transferring useful knowledge from one or more related source domains. Our experiments with the model have shown to improve state-of-the-art F1 score on 3 benchmark biomedical corpora for single domain and on 2 out of 3 for multi-domain scenarios. When used with contextualized embeddings, there is further boost in performance outperforming neural-network based domain adaptation baselines for both the cases. © 2019 Association for Computational Linguistics

关键词： Text processing

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Enabling Efficient Random Access to Hierarchically-Compressed Data

Enabling Efficient Random Access to Hierarchically-Compresse...

引用

International Conference on Data engineering

作者： Feng Zhang Jidong Zhai Xipeng Shen Onur Mutlu Xiaoyong Du Key Laboratory of Data Engineering and Knowledge Engineering (MOE) and School of Information Renmin University of China Department of Computer Science and Technology Tsinghua University BNRist Computer Science Department North Carolina State University Department of Computer Science ETH Zürich

ISBN: (数字)9781728129037

ISBN: (纸本)9781728129044

Recent studies have shown the promise of direct data processing on hierarchically-compressed text documents. By removing the need for decompressing data, the direct data processing technique brings large savings in both time and space. However, its benefits have been limited to data traversal operations; for random accesses, direct data processing is several times slower than the state-of-the-art baselines. This paper presents a set of techniques that successfully eliminate the limitation, and for the first time, establishes the feasibility of effectively handling both data traversal operations and random data accesses on hierarchically-compressed data. The work yields a new library, which achieves 3.1× speedup over the state-of-the-art on random data accesses to compressed data, while preserving the capability of supporting traversal operations efficiently and providing large (3.9×) space savings.

关键词： Indexing Data processing Data structures Technological innovation Dictionaries Law Data mining

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Extraction and comparative study of green energy using different types of biowaste material

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Materials Today: Proceedings 2022年 49卷 3474-3481页

作者： Palvinder Singh Cherry Bhargava Manish Kumar Abhilash Singh Maturia Pramod K. Singh Pardeep Kumar Sharma Amit Sachdeva Department of Electronics and IT Sanatan Dharma College Ambala 133001 Haryana India School of Electronics and Electrical Engineering Lovely Professional University Phagwara 144411 Punjab India Department of Computer Science and Engineering Symbiosis Institute of Technology Pune 412115 India Experimental Research Laboratory Department of Physics ARSD College University of Delhi New Delhi 110021 India Department of Life Sciences SBSR Sharda University Greater Noida 201310 Uttar Pradesh India Department of Physics Sharda University Knowledge Park-III Greater Noida 201 306 Uttar Pradesh India QC Analyst Cosmas Research Lab Ltd Hambran Punjab 141110 India

Wind and solar green energy sources have increasingly been widely used by businesses and researchers worldwide. In the literature, these energy sources have also been represented. For these developments to be universal, these energy sources need more construction costs, more maintenance costs, and more operating space. An energy storage system is to be built to solve these issues, allowing us to store energy during lean hours and supply energy at a low cost later. These technologies, such as the battery, cell, and supercapacitor, can very easily store energy and energy distribution speeds. This energy sources have a high current for a limited period. We need electrode material and electrolyte material mentioned in previous research papers to make these energy sources. Due to its excellent electrical conductivity, excellent stability, and high specific surface area, electrode materials such as carbon and carbon-based materials are generally used. In these energy sources, these materials serve as dielectric materials. Carbon and carbon-based materials have recently been derived from bio-waste materials. These bio-waste products are bio-waste for agriculture, bio-waste for tree leaves and bio-waste from fruits. The use of these bio-waste products aids in the disposal of waste and turns waste into a vital resource. These products for bio-waste are readily available at no cost. The approaches used by researchers to obtain from bio-waste are explored in this study research paper on the availability of different bio-waste materials.

关键词： Activation Energy density Power density Surface area Bio waste

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Social Group Query Based on Multi-fuzzy-constrained Strong Simulation

arXiv

引用

arXiv 2021年

作者： Liu, Guliu Li, Lei Liu, Guanfeng Wu, Xindong Key Laboratory of Knowledge Engineering with Big Data Hefei University of Technology Ministry of Education China School of Computer Science and Information Engineering Hefei University of Technology China Department of Computing Macquarie University Australia Mininglamp Academy of Sciences Mininglamp Technology China

Traditional social group analysis mostly uses interaction models, event models, or other methods to identify and distinguish groups. This type of method can divide social participants into different groups based on their geographic location, social relationships, and/or related events. However, in some applications, it is necessary to make more specific restrictions on the members and the interaction between members of the group. Generally, graph pattern matching (GPM) is used to solve this problem. However, the existing GPM methods rarely consider the rich contextual information of nodes and edges to measure the credibility between members. In this paper, a social group query problem that needs to consider the trust between members of the group is proposed. To solve this problem, we propose a Strong Simulation GPM algorithm (NTSS) based on the exploration of pattern Node Topological ordered sequence. Aiming at the inefficiency of the NTSS algorithm when matching pattern graph with multiple nodes with zero in-degree and the problem of repeated calculation of matched edges shared by multiple matching subgraphs, two optimization strategies are proposed. Finally, we conduct verification experiments on the effectiveness and efficiency of the NTSS algorithm and the algorithms with the optimization strategies on four social network datasets in real applications. Experimental results show that the NTSS algorithm is significantly better than the existing multi-constrained GPM algorithm, and the NTSS_Inv_EdgC algorithm, which combines two optimization strategies, greatly improves the efficiency of the NTSS algorithm. Copyright © 2021, The Authors. All rights reserved.

关键词： Constrained optimization

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