2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)最新文献_第3页

Determination of VEGF and CXCR4 in Tumor and Peritumoral Tissue of Patients with Breast Cancer as a Predictive Factor 乳腺癌患者肿瘤及瘤周组织中VEGF和CXCR4的检测作为预测因素

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635306

D. Cvetković, A. Cvetkovic, Danijela D. Nikodijević, Jovana V. Jovankić, Milena G. Milutinović, V. Stojić, N. Zdravković, Slobodanka Mltrović

引用次数: 0

A Gradient-based Approach for Explaining Multimodal Deep Learning Classifiers 一种基于梯度的多模态深度学习分类器解释方法

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635460

Charles A. Ellis, Rongen Zhang, V. Calhoun, Darwin A. Carbajal, Robyn Miller, May D. Wang

{"title":"A Gradient-based Approach for Explaining Multimodal Deep Learning Classifiers","authors":"Charles A. Ellis, Rongen Zhang, V. Calhoun, Darwin A. Carbajal, Robyn Miller, May D. Wang","doi":"10.1109/BIBE52308.2021.9635460","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635460","url":null,"abstract":"In recent years, more biomedical studies have begun to use multimodal data to improve model performance. Many studies have used ablation for explainability, which requires the modification of input data. This can create out-of-distribution samples and lead to incorrect explanations. To avoid this problem, we propose using a gradient-based feature attribution approach, called layer-wise relevance propagation (LRP), to explain the importance of modalities both locally and globally for the first time. We demonstrate the feasibility of the approach with sleep stage classification as our use-case and train a 1-D convolutional neural network with electroencephalogram (EEG), electrooculogram (EOG), and electromyogram (EMG) data. We also analyze the relationship of our local explainability results with clinical and demographic variables to determine whether they affect our classifier. Across all samples, EEG is the most important modality, followed by EOG and EMG. For individual sleep stages, EEG and EOG have higher relevance for awake and non-rapid eye movement 1 (NREM1). EOG is most important for REM, and EEG is most relevant for NREM2-NREM3. Also, LRP gives consistent levels of importance to each modality for the correctly classified samples across folds but inconsistent levels of importance for incorrectly classified samples. Our statistical analyses suggest that medication has a significant effect upon patterns learned for EEG and EOG NREM2 and that subject sex and age significantly affects the EEG and EOG patterns learned, respectively. Our results demonstrate the viability of gradient-based approaches for explaining multimodal electrophysiology classifiers and suggest their generalizability for other multimodal classification domains.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"222 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":"122884896","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: 7

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

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

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

Automatic Curvature Analysis for Finely Interpolated Spinal Curves 精细插值脊柱曲线的自动曲率分析

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635424

M. Neghina, R. Petruse, S. Ćuković, Caliri Schiau, Nenad Filipović

引用次数: 0

Computational Finite Element Analysis of Aortic Root with Bicuspid Valve 带二尖瓣主动脉根部的计算有限元分析

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635269

Smiljana Tomasevic, I. Šaveljić, L. Velicki, N. Filipovic

{"title":"Computational Finite Element Analysis of Aortic Root with Bicuspid Valve","authors":"Smiljana Tomasevic, I. Šaveljić, L. Velicki, N. Filipovic","doi":"10.1109/BIBE52308.2021.9635269","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635269","url":null,"abstract":"The aim of this work was to evaluate the impact of Bicuspid Aortic Valve (BAV), on displacements, Von Mises stress, shear stress and pressure distribution within the aortic root by using computational Finite Element (FE) method. The three-dimensional (3D) patient-specific geometry of dilated aortic root with BAV was reconstructed based on Computed Tomography (CT) scan images, in order to obtain the 3D finite element mesh. Two types of analyses: i) structural analysis and ii) computational fluid dynamics (CFD) were performed, with applied equivalent material characteristics of BAV and boundary conditions. The initial results for this single case, displacements and Von Mises stress distribution (for structural analysis), as well as shear stress and pressure distribution (for CFD analysis) were quantified concerning anatomical patient's structures. The regions of abnormal stresses on the aortic leaflets and annulus, with asymmetrically open bicuspid valve, were related to the increased pressures and shear stresses and analyzed for this patient-specific case. Due to the difficulties in obtaining such characteristics in vitro or in vivo, the performed computational analysis gave better insight into the biomechanics of the aortic root with BAV that is needed to achieve improvements in surgical repair techniques and presurgical planning.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"95 3 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":"116495222","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

A novel Greedy approach for Sequence based Computational prediction of Binding-Sites in Protein-Protein Interaction 一种基于序列的蛋白质相互作用结合位点计算预测的贪心方法

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635163

Aishwarya Purohit, S. Acharya, James Green

引用次数: 0

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