2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第4页

An FPGA-Based System for Video Processing to Detect Holes in Aquaculture Nets 基于fpga的水产网孔检测视频处理系统

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635351

Theofilos Zacheilas, K. Moirogiorgou, N. Papandroulakis, E. Sotiriades, M. Zervakis, A. Dollas

引用次数: 3

Scoring Primary Sjögren's syndrome affected salivary glands ultrasonography images by using deep learning algorithms 利用深度学习算法对原发性Sjögren综合征影响唾液腺超声图像进行评分

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635506

A. Vukicevic, A. Zabotti, V. Milic, A. Hočevar, O. Lucia, G. Filippou, A. Tzioufas, S. Vita, Nenad Filipović

{"title":"Scoring Primary Sjögren's syndrome affected salivary glands ultrasonography images by using deep learning algorithms","authors":"A. Vukicevic, A. Zabotti, V. Milic, A. Hočevar, O. Lucia, G. Filippou, A. Tzioufas, S. Vita, Nenad Filipović","doi":"10.1109/BIBE52308.2021.9635506","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635506","url":null,"abstract":"Salivary gland ultrasonography (SGUS) represents a promising tool for diagnosing Primary Sjögren's syndrome (pSS), which is manifest with abnormalities in salivary glands (SG). In this study, we propose a fully automatic method for scoring SGs in SGUS images, which is the most important step towards SG the pSS diagnosis. A two-centric cohort included 600 images (150 patients) annotated by experienced clinicians. The aim of the study was to assess various deep learning classifiers (MobileNetV2, VGG19, Dense-Net, Squeeze-Net, Inception_v3, and ResNet) for the purpose of the pSS scoring in SGUS. The training was performed using the ADAM optimizer and cross entropy loss function. Top performing algorithms were MobileNetV2, ResNet, and Dense-Net. The assessment showed that deep learning algorithms reached clinicians-level performances in the almost real-time. Considering that, the further work should be regarded towards evaluation on larger and international data sets with the goal to establish SGUS as an effective noninvasive pSS diagnostic tool.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"6 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130371675","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Aritificial Inteligence Challenges in COPD management: a review 人工智能在COPD管理中的挑战:综述

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635374

L. S. Becirovic, Amar Deumic, L. G. Pokvic, A. Badnjević

{"title":"Aritificial Inteligence Challenges in COPD management: a review","authors":"L. S. Becirovic, Amar Deumic, L. G. Pokvic, A. Badnjević","doi":"10.1109/BIBE52308.2021.9635374","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635374","url":null,"abstract":"Machine learning algorithms have been drawing attention in lung disease research. However, due to their algorithmic learning complexity and the variability of their architecture, there is an ongoing need to analyze their performance. This study reviews the input parameters and the performance of machine learning applied to diagnosis of chronic obstructive pulmonary disease (COPD). One research focus of this study was on clearly identifying problems and issues related to the implementation of machine learning in clinical studies. Following the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) protocol, 179, 1032, and 36,500 titles were identified from the PubMed, Scopus, and Google Scholar databases respectively. Studies that used machine learning to detect COPD and provided performance measures were included in our analysis. In the final analysis, 24 studies were included. The analysis of machine learning methods to detect COPD reveals the limited usage of the methods and the lack of standards that hinder the implementation of machine learning in clinical applications. The performance of machine learning for diagnosis of COPD was considered satisfactory for several studies; however, given the limitations indicated in our study, further studies are warranted to extend the potential use of machine learning to clinical settings.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"57 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128412919","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 2

A Comparative Analysis of Breast Cancer Diagnosis by Fusing Visual and Semantic Feature Descriptors 融合视觉和语义特征描述符诊断乳腺癌的比较分析

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635481

G. Apostolopoulos, A. Koutras, D. Anyfantis, Ioanna Christoyianni

{"title":"A Comparative Analysis of Breast Cancer Diagnosis by Fusing Visual and Semantic Feature Descriptors","authors":"G. Apostolopoulos, A. Koutras, D. Anyfantis, Ioanna Christoyianni","doi":"10.1109/BIBE52308.2021.9635481","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635481","url":null,"abstract":"Computer-aided Diagnosis (CAD) systems have become a significant assistance tool, that are used to help identify abnormal/normal regions of interest in mammograms faster and more effectively than human readers. In this work, we propose a new approach for breast cancer identification of all type of lesions in digital mammograms by combining low-and high-level mammogram descriptors in a compact form. The proposed method consists of two major stages: Initially, a feature extraction process that utilizes two dimensional discrete transforms based on ART, Shapelets and textural representations based on Gabor filter banks, is used to extract low-level visual descriptors. To further improve our method's performance, the semantic information of each mammogram given by radiologists is encoded in a 16-bit length word high-level feature vector. All features are stored in a quaternion and fused using the L2 norm prior to their presentation to the classification module. For the classification task, each ROS is recognized using two different classification models, Ada Boost and Random Forest. The proposed method is evaluated on regions taken from the DDSM database. The results show that Ada Boost outperforms Random Forest in terms of accuracy (99.2%$(pm 0.527)$ against 93.78% $(pm 1.659))$, precision, recall and F-measure. Both classifiers achieve a mean accuracy of 33% and 38% higher than using only visual descriptors, showing that semantic information can indeed improve the diagnosis when it is combined with standard visual features.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"100 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134315547","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Predicting Multi-Epitope Vaccine Candidates Using Natural Language Processing and Deep Learning 基于自然语言处理和深度学习的多表位候选疫苗预测

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635304

Xiaozhi Yuan, Daniel Bibl, Kahlil Khan, Lei Sun

引用次数: 0

Smart Protection from Electricity Hazards in Children's Room 儿童房电气危险智能防护

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635230

Filippos Bitsas, Irini Georgia Dimitriou, G. Manis

引用次数: 0

Computational Modeling of Sarcomere Protein Mutations and Drug Effects on Cardiac Muscle Behavior 肌节蛋白突变的计算模型和药物对心肌行为的影响

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635428

Momcilo Prodanovic, B. Stojanovic, Danica Prodanovic, N. Filipovic, S. Mijailovich

引用次数: 1

Convolutional Neural Networks for Cellular Drug Response Prediction Using Immunofluorescence Images of Intracellular Actin Filament Networks 基于细胞内肌动蛋白丝网络免疫荧光图像的卷积神经网络药物反应预测

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635241

R. W. Oei, Jiewen Zhang, Jin Zhong, Guanqun Hou, Nuntawat Chanajarunvit, N. Xu

{"title":"Convolutional Neural Networks for Cellular Drug Response Prediction Using Immunofluorescence Images of Intracellular Actin Filament Networks","authors":"R. W. Oei, Jiewen Zhang, Jin Zhong, Guanqun Hou, Nuntawat Chanajarunvit, N. Xu","doi":"10.1109/BIBE52308.2021.9635241","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635241","url":null,"abstract":"Actin cytoskeleton has been identified as a potential therapeutic target for cancer. Therefore, to identify cell responses to such chemical agents has been an essential part in the past studies, which is often measured visually. This kind of visual recognition task currently is performed by human experts, which poses a great challenge since the features can hardly be detected using only human eyes. This article presents the application of convolutional neural networks (CNNs) in classifying human breast epithelial cells based on different dosages of drug exposure. MCF-10A cell line was chosen for the experiments and was treated with 90 nM and 400 nM cytochalasin D. The CNNs were evaluated on a large immunofluorescence images of intracellular actin filament networks captured after the exposure of different drug concentrations. During the image pre-processing, we implemented image enhancement and data augmentation approaches. Two well-known CNNs, VGG-16 and ResNet-50, were trained with or without transfer learning. The study revealed that the CNN performed better in the classification task compared to human experts. In conclusion, ResN et-50 with transfer learning achieved the best performance.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"46 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123596548","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Estimation of antiradical properties of series of 4, 7 - dihydroxycoumarin derivatives towards DPPH radical-experimental and DFT study 一系列4,7 -二羟基香豆素衍生物对DPPH自由基的抗自由基性能评价-实验和DFT研究

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635257

Žiko B. Milanović, Edina H. Avdović, Dušica M Simijonović, Z. Marković

{"title":"Estimation of antiradical properties of series of 4, 7 - dihydroxycoumarin derivatives towards DPPH radical-experimental and DFT study","authors":"Žiko B. Milanović, Edina H. Avdović, Dušica M Simijonović, Z. Marković","doi":"10.1109/BIBE52308.2021.9635257","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635257","url":null,"abstract":"Different phenolic coumarin derivatives represent a widespread class of compounds that have shown remarkable activity in removing reactive oxygen species. For this reason, within this study, the antiradical activity of previously synthesized phenolic derivatives of 4,7 -dihydroxycoumarin: (E)-3-(1-((2-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-20H), $(E)$ -3-(1((3-hydroxyphenyl)amino)ethylidene)-2,4-dioxochroman-7-yl acetate (A-30H), $(E)$. -3-(1((4-hydroxyphenyl)amino) ethylidene) -2,4-dioxochroman-7-yl (A-40H) acetate against the 2,2-diphenyl-1-picrylhydrazyl (DPPH·) radical was investigated. All research is supported by Density Functional Theory $(mathbf{DFT}/mathbf{M06}-mathbf{2X/6-311++}mathbf{G}(mathbf{d, p})$ level of theory and CPCM solvation model-methanol) in combination with global chemical reactivity parameters. The results of experimental scavenging activity towards DPPH· indicate that A-20H shows the best activity. The most probable scavenging route was determined based on the thermodynamic parameters. A good correlation between experiment and theory showed that Hydrogen Atom Transfer (HAT, $Deltatext{rGHAT}$) was the dominant pathway of the reduction of DPPH·. In general, the results of global chemical reactivity parameters show that the A-40H compound shows the best electron-donating properties, which is correlated with thermodynamic parameters obtained for the Single Electron Transfer (SET, $Delta{text{rGSET}}$) mechanism.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"35 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123699361","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}

引用次数: 0

Clustering based Segmentation of MR Images for the Delineation and Monitoring of Multiple Sclerosis Progression 基于聚类分割的磁共振图像用于多发性硬化症进展的描绘和监测

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635369

Styliani P. Zelilidou, E. Tripoliti, Kostas I. Vlachos, S. Konitsiotis, D. Fotiadis

引用次数: 0