2020 International Conference on Systems, Signals and Image Processing (IWSSIP)最新文献

Graphical Oracles to Assess Computer-Aided Diagnosis Systems: A Case Study in Mammogram Masses and Calcifications Detection 评估计算机辅助诊断系统的图形预测:乳房x光片肿块和钙化检测的案例研究

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145054

Vagner Mendonça Gonçalves, M. Delamaro, Fátima L. S. Nunes

引用次数: 0

EvolveDTree: Analyzing Student Dropout in Universities EvolveDTree:分析大学学生的辍学率

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145203

G. Santos, Kele T. Belloze, Luís Tarrataca, D. B. Haddad, A. Bordignon, Diego N. Brandão

{"title":"EvolveDTree: Analyzing Student Dropout in Universities","authors":"G. Santos, Kele T. Belloze, Luís Tarrataca, D. B. Haddad, A. Bordignon, Diego N. Brandão","doi":"10.1109/IWSSIP48289.2020.9145203","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145203","url":null,"abstract":"Brazilian society suffers constant financial losses when higher education students disassociate from universities without completing the degree program in which they were enrolled. This is especially true when the institutions are funded through public resources. In order to minimize evasion losses, socioeconomic policies and programs were created to assist and support actions seeking to maximize the number of students that graduate in a suitable program time. This work presents a methodology that aims to predict evasion by using machine learning. Our approach was able to classify student abandonment with an average f-score and accuracy results above 95%. Our approach combines a decision tree alongside a genetic algorithm and cluster stratified sampling. The results obtained show that students with a Grade Point Average (GPA) below 5.79 and that have been enrolled for more than a year require careful monitoring because they tend to exceed the program duration time or abandon it. Furthermore, approximately one-third of all identified dropouts students occurred in the first year.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"19 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117266325","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

Demand-Side Management Framework for Smart Cities 智慧城市需求侧管理框架

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145058

Fabio Mentzingen, Wilian Martins, Raphael Alves, Yona Lopes

引用次数: 0

Functional and Effective Connectivity Characterization of Absence Seizures 失神发作的功能和有效连接特征

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145352

Viviane S. G. M. Tenório, J. Assis, F. M. Assis

{"title":"Functional and Effective Connectivity Characterization of Absence Seizures","authors":"Viviane S. G. M. Tenório, J. Assis, F. M. Assis","doi":"10.1109/IWSSIP48289.2020.9145352","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145352","url":null,"abstract":"The human brain is a complex system and many approaches are currently being used to study its disorders. Functional Connectivity is a well-known approach which characterizes the relationship between two or more regions of the brain. This relationship is inferred from measuring and correlating activity from the brain. On the other hand, Effective Connectivity quantifies activities instead of correlating them. For Functional Connectivity, Phase Locked Value (PLV) and other measures are used. For Effective Connectivity, Transfer Entropy is the main measure used. The problem is: Functional Connectivity describes the flow of information in the complex network of the brain in terms of correlation, and allows time delays computations for better correlations - but gives no information regarding causality; Transfer Entropy is very accurate regarding causality, but has assessed interactions at only one time delay. This paper will address these issues by analysing a data set of absence seizures on both connectivity analyses. PLV (Phase Locking Value) and Transfer Entropy (TE) algorithms will be performed on EEG data from 11 subjects diagnosed with Absence Seizures. Afterwards, both networks will be described in terms of each connectivity type. The main contribution of this paper is that the network specification of both Functional and Effective Connectivity are complementary when characterizing absence seizures from EEG data.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"55 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122276625","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

Main Track 主要跟踪

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/iwssip48289.2020.9145222

引用次数: 0

DuaLAnet: Dual Lesion Attention Network for Thoracic Disease Classification in Chest X-Rays DuaLAnet:胸部x光片中胸部疾病分类的双重病变注意网络

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145037

Vinicius Teixeira, Leodécio Braz, H. Pedrini, Zanoni Dias

{"title":"DuaLAnet: Dual Lesion Attention Network for Thoracic Disease Classification in Chest X-Rays","authors":"Vinicius Teixeira, Leodécio Braz, H. Pedrini, Zanoni Dias","doi":"10.1109/IWSSIP48289.2020.9145037","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145037","url":null,"abstract":"The chest radiography is one of the most accessible radiological examinations for diagnosis of lung and heart diseases. Deep learning techniques have been increasingly used to provide more accurate detection of thorax lesions on Chest X-Ray (CXR) images. However, we observe that we can use the complementarity of dual asymmetric deep convolutional neural networks (DCNNs) to improve the ability of CXR image classification compared to the single network. In this paper, we propose a novel dual lesion attention network named DuaLAnet for the classification of 14 thorax diseases on chest radiography. The DuaLAnet consists of two asymmetric attention networks, DenseNet-169 and ResNet-152, to integrate the advantages into a wider architecture, thus extracting more discriminative features of different abnormalities from the raw CXRs. Moreover, a training strategy is designed to integrate the loss contribution of the involved classifiers into a unified loss. The proposed DuaLAnet has been evaluated against eight deep learning models using the patient-wise official split of the ChestX-ray14 dataset [1]. Our results show that DuaLAnet achieves and average per-class AUC of 0.820 in the experiments, which clearly substantiate the effectiveness of DuaLAnet when compared to the state-of-the-art baselines.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"22 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129963247","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: 11

Cubic Law and MAP Compensation Techniques for Robust Text-Independent Speaker Identification 基于三次律和MAP补偿的鲁棒文本独立说话人识别

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145319

Harry Anacleto, David Chavez

引用次数: 1

Blank Page 空白页

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/iwssip48289.2020.9145470

引用次数: 0

A Survey on Performance Metrics for Object-Detection Algorithms 目标检测算法的性能指标综述

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145130

Rafael Padilla, S. L. Netto, Eduardo A. B. da Silva

{"title":"A Survey on Performance Metrics for Object-Detection Algorithms","authors":"Rafael Padilla, S. L. Netto, Eduardo A. B. da Silva","doi":"10.1109/IWSSIP48289.2020.9145130","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145130","url":null,"abstract":"This work explores and compares the plethora of metrics for the performance evaluation of object-detection algorithms. Average precision (AP),for instance, is a popular metric for evaluating the accuracy of object detectors by estimating the area under the curve (AUC) of the precision × recall relationship. Depending on the point interpolation used in the plot, two different AP variants can be defined and, therefore, different results are generated. AP has six additional variants increasing the possibilities of benchmarking. The lack of consensus in different works and AP implementations is a problem faced by the academic and scientific communities. Metric implementations written in different computational languages and platforms are usually distributed with corresponding datasets sharing a given bounding-box description. Such projects indeed help the community with evaluation tools, but demand extra work to be adapted for other datasets and bounding-box formats. This work reviews the most used metrics for object detection detaching their differences, applications, and main concepts. It also proposes a standard implementation that can be used as a benchmark among different datasets with minimum adaptation on the annotation files.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114988682","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: 434

Automatic Prostate Segmentation on 3D MRI Scans Using Convolutional Neural Networks with Residual Connections and Superpixels 基于残差连接和超像素卷积神经网络的三维MRI前列腺自动分割

2020 International Conference on Systems, Signals and Image Processing (IWSSIP) Pub Date : 2020-07-01 DOI: 10.1109/IWSSIP48289.2020.9145218

G. L. F. D. Silva, J. V. F. França, P. S. Diniz, A. Silva, A. Paiva, E. A. A. D. Cavalcanti

{"title":"Automatic Prostate Segmentation on 3D MRI Scans Using Convolutional Neural Networks with Residual Connections and Superpixels","authors":"G. L. F. D. Silva, J. V. F. França, P. S. Diniz, A. Silva, A. Paiva, E. A. A. D. Cavalcanti","doi":"10.1109/IWSSIP48289.2020.9145218","DOIUrl":"https://doi.org/10.1109/IWSSIP48289.2020.9145218","url":null,"abstract":"Automatic and reliable prostate segmentation is an essential prerequisite for assisting the diagnosis and treatment, such as guiding biopsy procedure and radiation therapy. Notwithstanding, automatic segmentation is challenging due to the lack of clear prostate boundaries owing to similar appearance of prostate and surrounding tissues and the wide variation in size and shape among different patients. Therefore, this paper proposes an automatic method for prostate segmentation on 3D MRI scans using a content-sensitive superpixels technique, deep convolutional neural networks with residual connections (ResCNN), and the particle swarm optimization (PSO) algorithm. The proposed method has been evaluated on the Prostate 3T and PROMISE12 databases, presenting a Dice similarity coefficient of 86.68 %, Jaccard index of 76.58%, relative volume difference of 3.92%, volumetric similarity of 96.69 %, sensitivity of 88.36 %, specificity of 93.43%, accuracy of 91.97% and an area under ROC curve of 90.90 %. Experimental results demonstrate the high performance-potential of the proposed method comparable to those previously published.","PeriodicalId":406449,"journal":{"name":"2020 International Conference on Systems, Signals and Image Processing (IWSSIP)","volume":"129 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-07-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128287060","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: 3