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

Gastric Location Classification During Esophagogastroduodenoscopy Using Deep Neural Networks 基于深度神经网络的食管胃十二指肠镜胃定位分类

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635273

A. Ding, Ying Li, Qilei Chen, Yu Cao, Benyuan Liu, Shu Han Chen, Xiaowei Liu

{"title":"Gastric Location Classification During Esophagogastroduodenoscopy Using Deep Neural Networks","authors":"A. Ding, Ying Li, Qilei Chen, Yu Cao, Benyuan Liu, Shu Han Chen, Xiaowei Liu","doi":"10.1109/BIBE52308.2021.9635273","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635273","url":null,"abstract":"Esophagogastroduodenoscopy (EGD) is a common procedure that visualizes the esophagus, stomach, and the duodenum by inserting a camera, attached to a long flexible tube, into the patient's mouth and down the stomach. A comprehensive EGD needs to examine all gastric locations, but since the camera is controlled manually, it is easy to miss some surface area and create diagnostic blind spots, which often result in life-costing oversights of early gastric cancer and other serious illnesses. In order to address this problem, we train a convolutional neural network to classify gastric locations based on the camera feed during an EGD, and based on the classifier and a triggering algorithm we propose, we build a video processing system that checks off each location as visited, allowing human operators to keep track of which locations they have visited and which they have not. Based on collected clinical patient reports, we consider six gastric locations, and we add a background class to our classifier to accomodate for the frames in EGD videos that do not resemble the six defined classes (including when the camera is outside of the patient body). Our best classifier achieves 98 % accuracy within the six gastric locations and 88 % accuracy including the background class, and our video processing system clearly checks off gastric locations in an expected order when being tested on recorded EGD videos. Lastly, we use class activation mapping to provide human-readable insight into how our trained classifier works.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"57 50","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2021-10-25","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"120839570","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: 4

Sparse Graph-based Representations of SSVEP Responses Under the Variational Bayesian Framework 变分贝叶斯框架下基于稀疏图的SSVEP响应表示

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635427

V. Oikonomou, S. Nikolopoulos, Y. Kompatsiaris

引用次数: 2

Heart Rate Classification Using ECG Signal Processing and Machine Learning Methods 基于心电信号处理和机器学习方法的心率分类

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635462

M. Papadogiorgaki, M. Venianaki, Paulos Charonyktakis, M. Antonakakis, I. Tsamardinos, M. Zervakis, V. Sakkalis

{"title":"Heart Rate Classification Using ECG Signal Processing and Machine Learning Methods","authors":"M. Papadogiorgaki, M. Venianaki, Paulos Charonyktakis, M. Antonakakis, I. Tsamardinos, M. Zervakis, V. Sakkalis","doi":"10.1109/BIBE52308.2021.9635462","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635462","url":null,"abstract":"Electrocardiogram (ECG) signal constitutes a valuable technique that provides considerable information towards the early diagnosis of several cardiovascular diseases, especially regarding the detection of abnormal heart rate, namely arrhythmias. In this paper, innovative methodologies that allow for the efficient classification of cardiac rhythm are presented. The proposed methods are based on ECG signal analysis, extraction of significant features, as well as classification algorithms. Several clinical, time- and frequency-domain features are either calculated, or automatically extracted by means of a Convolutional Neural Network, while traditional machine learning algorithms, such as k-Nearest Neighbors and Random Forests are employed in order to classify the ECG signals among 7 different cases of abnormal and normal heart rate. The learning methods are carried out within the JADBio software tool, that also performs feature selection prior to classification. The experimental results demonstrate high performance of the deployed methods in terms of relevant statistical metrics, while they yielded an average validation Area Under the Curve (AUC) of 99.9%.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"22 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":"115018126","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: 5

Analyzing the Impact of Resampling Approaches on Chest X-Ray Images for COVID-19 Identification in a Local Hierarchical Classification Scenario 局部分层分类场景下重采样方法对胸部x线图像COVID-19识别的影响分析

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635433

F. K. H. D. Barros, André L. Jeller Selleti, Vinicius Queiroz, R. M. Pereira, C. Silla

{"title":"Analyzing the Impact of Resampling Approaches on Chest X-Ray Images for COVID-19 Identification in a Local Hierarchical Classification Scenario","authors":"F. K. H. D. Barros, André L. Jeller Selleti, Vinicius Queiroz, R. M. Pereira, C. Silla","doi":"10.1109/BIBE52308.2021.9635433","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635433","url":null,"abstract":"Researchers dealing with real-world data - such as in the healthcare domain - tend to face class imbalance issues. More specifically, publicly available datasets containing Chest X-Ray (CXR) of Pneumonia diseases (including COVID-19) usually have an imbalanced class distribution. This dataset imbalance causes automatic diagnosis systems to classify majority classes with much more accuracy than the minority ones. Several resampling algorithms were proposed in the past to deal with the class imbalance issue. Hierarchical classifiers have also been proposed to increase the predictive performance of classifiers, but there is little research in the literature verifying if using existing resampling algorithms with hierarchical classifiers are a good alternative to improve classification performance. This work proposes an experimental classification schema to investigate the effectiveness of using resampling algorithms in the identification of COVID-19 and other types of Pneumonia through CXR images. The proposed schema uses resampling algorithms to rebalance the class distribution, in a Local Hierarchical Classification scenario. The experimental evaluation, which is supported by inferential statistical analysis, showed that using specific resampling algorithms with Local Hierarchical Classifiers brings a statistically significant increase to the macro-averaged Fl-Score, and improves the predictive performance for the minority classes.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"243 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":"114155860","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

Theory and Parameterization of Infections and Waves by Covid-19: A 6-Countries Data Analysis Covid-19感染和波的理论和参数化:一项6国数据分析

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635536

H. Nieto-Chaupis

引用次数: 2

CareKeeper: A Platform for Intelligent Care Coordination CareKeeper:智能护理协调平台

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635445

H. Kondylakis, Dimitrios G. Katehakis, A. Kouroubali, K. Marias, G. Flouris, Theodoros Patkos, I. Fundulaki, D. Plexousakis

引用次数: 2

Epidemiological forecasting of COVID-19 infection using deep learning approach 基于深度学习方法的COVID-19感染流行病学预测

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635289

A. Blagojević, T. Šušteršič, Nenad Filipović

引用次数: 0

Autoencoder-based bone removal algorithm from x-ray images of the lung 基于自编码器的肺x线图像去骨算法

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635451

Seweryn Kalisz, M. Marczyk

{"title":"Autoencoder-based bone removal algorithm from x-ray images of the lung","authors":"Seweryn Kalisz, M. Marczyk","doi":"10.1109/BIBE52308.2021.9635451","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635451","url":null,"abstract":"The application of machine learning methods in biomedical image analysis has recently become of particular interest to researchers. One of the most common diagnostic methods with low cost and high availability is X-ray imaging. It allows the acquisition of frontal images of the chest, which can be used in the medical diagnosis of various diseases and prognosis. Due to the presence of ribs on the image, some pathologic changes may go unnoticed. The goal of this work is to develop a method, using deep learning techniques, to remove ribs from chest X-ray images. The Bone Suppression dataset, consisting of 35 pairs of standard X-ray and soft-tissue only images, was used to develop the model. COVIDx was used as an external test set. Due to the small number of images in the training cohort, a data augmentation technique was used to generate new, noisy image pairs. A deep learning model using convolutional denoising autoencoder architecture was developed to remove the ribs from the X-ray image. The effects of two image down-sampling methods and learning rate changes were evaluated. The resulting images are characterized by partial or complete suppression of the ribs. It should be noted that the problem was not posed by images of patients suffering from COVID-19, which are characterized by much more complex structures.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"41 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":"128138688","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

Calculation of blood flow in carotid artery bifurcation by turbulent finite element method 用湍流有限元法计算颈动脉分叉处的血流

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635360

A. Nikolic, M. Topalovic, V. Simić, N. Filipovic

引用次数: 0

Free radical scavenger capacity of 1,2,5-trihydroxyanthraquinone and 1,2,5-trihydroxythioxanthone: a theoretical comparative study 1,2,5-三羟基蒽醌与1,2,5-三羟基硫氧蒽酮自由基清除能力的理论比较研究

2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE) Pub Date : 2021-10-25 DOI: 10.1109/BIBE52308.2021.9635259

S. Jeremic, M. S. Pirkovic, Jelena Đorović Jovanović, Z. Marković

{"title":"Free radical scavenger capacity of 1,2,5-trihydroxyanthraquinone and 1,2,5-trihydroxythioxanthone: a theoretical comparative study","authors":"S. Jeremic, M. S. Pirkovic, Jelena Đorović Jovanović, Z. Marković","doi":"10.1109/BIBE52308.2021.9635259","DOIUrl":"https://doi.org/10.1109/BIBE52308.2021.9635259","url":null,"abstract":"In this contribution are estimated and compared free radical scavenger capacity of 1,2,5- trihydroxyanthraquinone (AN) and 1,2,5-trihydroxythioxanthone (TX). For this purpose, $mathbf{M06}-mathbf{2X/6}-311++mathbf{G}(mathbf{d, p})$ method is used. Scavenger capacities of both molecules are determined in benzene and water. It is found that both antioxidants generate stable radicals in water following SPLET mechanism. On the other hand, the most plausible mechanism for that purpose in benzene is HAT. In the presence of three selected free radicals $(mathbf{HO}^{bullet},mathbf{HOO}^{bullet}mathbf{and} mathbf{CH_{3}OO}^{bullet})$ these molecules manifest their scavenger capacity following HAT and SPLET competitively in both estimated environment conditions. The reactivity of observed molecules toward free radicals decreases following the order: $mathbf{HO}^{bullet}ggmathbf{HOO}^{bullet}>mathbf{CH_{3}OO}^{bullet}$. Comparing thermodynamic parameters that describe homolytic O-H cleavage for estimated antioxidants, it is concluded that TX shows somewhat higher scavenger capacity.","PeriodicalId":343724,"journal":{"name":"2021 IEEE 21st International Conference on Bioinformatics and Bioengineering (BIBE)","volume":"19 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":"115457300","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