2020 International Conference on Machine Vision and Image Processing (MVIP)最新文献_第5页

Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis 基于pix2pixel的染色到染色转换:组织病理学图像分析中鲁棒染色归一化的解决方案

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116895

Pegah Salehi, A. Chalechale

{"title":"Pix2Pix-based Stain-to-Stain Translation: A Solution for Robust Stain Normalization in Histopathology Images Analysis","authors":"Pegah Salehi, A. Chalechale","doi":"10.1109/MVIP49855.2020.9116895","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116895","url":null,"abstract":"The diagnosis of cancer is mainly performed by visual analysis of the pathologists, through examining the morphology of the tissue slices and the spatial arrangement of the cells. If the microscopic image of a specimen is not stained, it will look colorless and textured. Therefore, chemical staining is required to create contrast and help identify specific tissue components. During tissue preparation due to differences in chemicals, scanners, cutting thicknesses, and laboratory protocols, similar tissues are usually varied significantly in appearance. This diversity in staining, in addition to Interpretive disparity among pathologists more is one of the main challenges in designing robust and flexible systems for automated analysis. To address the staining color variations, several methods for normalizing stain have been proposed. In our proposed method, a Stain-to-Stain Translation (STST) approach is used to stain normalization for Hematoxylin and Eosin (H&E) stained histopathology images, which learns not only the specific color distribution but also the preserves corresponding histopathological pattern. We perform the process of translation based on the \"pix2pix\" framework, which uses the conditional generator adversarial networks (cGANs). Our approach showed excellent results, both mathematically and experimentally against the state of the art methods. We have made the source code publicly available 1.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122250505","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: 56

Brain MR Image Classification for ADHD Diagnosis Using Deep Neural Networks 脑MR图像分类在ADHD诊断中的应用

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116877

Sahar Abdolmaleki, M. S. Abadeh

{"title":"Brain MR Image Classification for ADHD Diagnosis Using Deep Neural Networks","authors":"Sahar Abdolmaleki, M. S. Abadeh","doi":"10.1109/MVIP49855.2020.9116877","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116877","url":null,"abstract":"Attention-deficit/hyperactivity disorder (ADHD) is one of the most prevalent neurodevelopmental disorder in childhood and adolescence. ADHD diagnosis currently includes psychological tests and depends on ratings of behavioral symptoms, which can be unreliable. Thus, an objective diagnostic tool based on non-invasive imaging can improve the understanding and diagnosis of ADHD. The purpose of this study is classifying brain images by using Artificial Intelligence methods such as clinical decision support system for the diagnosis of ADHD. For this purpose and according to a medical imaging classification system, firstly, image pre-processing is done. Then, a deep multi-modal 3D CNN is trained on GM from structural and fALFF from functional MRI using ADHD-200 training dataset. Finally, with the intention of classifying the extracted features, early and late fusion schemes are employed, and the output scores are classified with the SVM, KNN and LDA algorithms. The evaluation of the proposed approach on the ADHD-200 testing dataset revealed that the presence of personal characteristics alone increased the classification accuracy by 3.79%. In addition, using a combination of early, late fusion and personal characteristics together improved the accuracy of the classification by 5.84%. Among the three classifiers LDA showed better results and achieved a classification accuracy of 74.93%. The comparison of results showed that the combination of early and late fusion as well as considering personal characteristics has a significant effect on enhancing classification accuracy. As a result of this, the reliability of this medical decision support system is increased.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"150 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116369876","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

High-Resolution Document Image Reconstruction from Video 从视频重建高分辨率文档图像

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116907

Hossein Motamednia, Mohammad Minouei, Pooryaa Cheraaqee, M. Soheili

引用次数: 0

Fully Convolutional Networks for Fluid Segmentation in Retina Images 视网膜图像流体分割的全卷积网络

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116914

Behnam Azimi, A. Rashno, S. Fadaei

引用次数: 4

Ensemble P-spectral Semi-supervised Clustering 集合p谱半监督聚类

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116885

S. Safari, F. Afsari

引用次数: 1

Random Forest with Attribute Profile for Remote Sensing Image Classification 基于属性轮廓的随机森林遥感图像分类

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116878

M. Imani

引用次数: 1

EEG-based Motor Imagery Classification through Transfer Learning of the CNN 基于脑电图的CNN迁移学习运动图像分类

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116900

Saman Taheri, M. Ezoji

引用次数: 2

Offline Handwritten Signature Verification Based on Circlet Transform and Statistical Features 基于圆变换和统计特征的离线手写签名验证

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116909

A. Foroozandeh, A. A. Hemmat, H. Rabbani

引用次数: 2

Image Colorization Using Generative Adversarial Networks and Transfer Learning 使用生成对抗网络和迁移学习的图像着色

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116882

Leila Kiani, Masoudnia Saeed, H. Nezamabadi-pour

{"title":"Image Colorization Using Generative Adversarial Networks and Transfer Learning","authors":"Leila Kiani, Masoudnia Saeed, H. Nezamabadi-pour","doi":"10.1109/MVIP49855.2020.9116882","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116882","url":null,"abstract":"Automatic colorizing is one of the most interesting problems in computer graphics. During the colorization process, the gray one-dimensional images are converted to three-dimensional images with colored components. As a typical technique, Convolutional neural networks (CNNs) have been well studied and used for automatic coloring. In these networks, the information that is generalized over in the top layers is available in intermediate layers. Although the output of the last layer of CNNs is usually used in many applications, in this paper, we use a concept called \"Hypercolumn\" derived from neuroscience to exploit information at all levels to develop a fully automated image colorization system. There are not always millions of data available in the real world to train complex deep learning models. Therefore, the VGG19 model trained with the big data set of ImageNet is used as a pre-trained model in the generator network and the hypercolumn idea is implemented in it with DIV2K datasets. We train our model to predict each pixel’s color texture. The results obtained indicate that the proposed method is superior to competing models.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"65 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126644287","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: 10

Deep Learning based Classification of Color Point Cloud for 3D Reconstruction of Interior Elements of Buildings 基于深度学习的颜色点云分类在建筑室内元素三维重建中的应用

2020 International Conference on Machine Vision and Image Processing (MVIP) Pub Date : 2020-02-01 DOI: 10.1109/MVIP49855.2020.9116894

Shima Sahebdivani, H. Arefi, M. Maboudi

{"title":"Deep Learning based Classification of Color Point Cloud for 3D Reconstruction of Interior Elements of Buildings","authors":"Shima Sahebdivani, H. Arefi, M. Maboudi","doi":"10.1109/MVIP49855.2020.9116894","DOIUrl":"https://doi.org/10.1109/MVIP49855.2020.9116894","url":null,"abstract":"In architecture and engineering, the production of 3D models of various objects that are both simple and most closely related to reality is of particular importance. In this article, we are going to model different aspects of the interior of a building, which is performed in three general steps. In the first step, the existing point clouds of a room are semantically segmented using the PointNet Deep Learning Network. Each class of objects is then reconstructed using three methods including: Poisson, ball-pivoting and combined volumetric triangulation method and marching cubes. In the last step, each model is simplified by the methods of vertex clustering and edge collapse with quadratic error. Results are quantitatively and qualitatively evaluated for two types of objects, one with simple geometry and one with complex geometry. After selecting the optimal surface reconstruction method and simplifying it, all the objects are modeled. According to the results, the Poisson surface reconstruction method with a simplified edge collapse method provides better geometric accuracy of 0.1 mm for simpler geometry classes. In addition, for more complex geometry problems, the model produced by combined volumetric triangulation method and marching cubes with simplified edge collapse method was more suitable due to a higher accuracy of 0.022 mm.","PeriodicalId":255375,"journal":{"name":"2020 International Conference on Machine Vision and Image Processing (MVIP)","volume":"148 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"128601939","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