2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)最新文献_第3页

Systematic Analysis And Automated Search Of Hyper-Parameters For Cell Classifier Training 细胞分类器训练超参数的系统分析与自动搜索

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153408

Philipp Gräbel, Gregor Nickel, M. Crysandt, Reinhild Herwartz, Melanie Baumann, B. Klinkhammer, P. Boor, T. Brümmendorf, D. Merhof

引用次数: 1

Topological Signal Processing in Neuroimaging Studies 神经影像学研究中的拓扑信号处理

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153363

Yuan Wang, R. Behroozmand, L. Johnson, L. Bonilha, J. Fridriksson

引用次数: 2

ISBI Workshops 2020 Technical Table of Contents ISBI研讨会2020技术目录

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/isbiworkshops50223.2020.9153452

引用次数: 0

3D Few-View CT Image Reconstruction with Deep Learning 基于深度学习的三维少视图CT图像重建

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153411

Huidong Xie, Hongming Shan, Ge Wang

引用次数: 2

Cerebral Microbleed Detection Via Fourier Descriptor with Dual Domain Distribution Modeling 基于对偶域分布建模的傅里叶描述子脑微出血检测

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153365

Hangfan Liu, T. Rashid, M. Habes

引用次数: 11

A Decision Support System For Retinal Image Defect Detection 视网膜图像缺陷检测的决策支持系统

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153446

Aparna Kanakatte, J. Gubbi, Avik Ghose, B. Purushothaman

{"title":"A Decision Support System For Retinal Image Defect Detection","authors":"Aparna Kanakatte, J. Gubbi, Avik Ghose, B. Purushothaman","doi":"10.1109/ISBIWorkshops50223.2020.9153446","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153446","url":null,"abstract":"Deep learning has become the de facto method for image classification. In this work, a common framework for decision support system is presented that can be reused for diagnosing multiple retinal clinical conditions. Retinal fundus images provide a non-invasive way to diagnose eye-related diseases like glaucoma and diabetic retinopathy (DR). State-of-the-art deep learning methods focus on the detection of key regions of the retina including fundus, optic disc and retinal vessels individually. In order to achieve acceptable precision and recall for a clinically deployable system, a decision support system that combines state-of-the-art deep learning system and relevant explainable features are built. The proposed method is tested on two retinal pathology use cases - glaucoma and for the detection of hard exudates that is critical in diagnosing DR. The proposed model is validated using DRIVE dataset with average Jaccard index of more than 96% for fundus, around 98% for OD and around 90% in identifying retinal vessels using a five-fold cross-validation. For disease detection, the above key regions are combined and validated using standard datasets with good outcomes.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"50 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116760534","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

Deep Quantized Representation For Enhanced Reconstruction 增强重建的深度量化表示

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153414

Akash Gupta, Abhishek Aich, Kevin Rodriguez, G. Reddy, A. Roy-Chowdhury

{"title":"Deep Quantized Representation For Enhanced Reconstruction","authors":"Akash Gupta, Abhishek Aich, Kevin Rodriguez, G. Reddy, A. Roy-Chowdhury","doi":"10.1109/ISBIWorkshops50223.2020.9153414","DOIUrl":"https://doi.org/10.1109/ISBIWorkshops50223.2020.9153414","url":null,"abstract":"While machine learning approaches have shown remarkable performance in biomedical image analysis, most of these methods rely on high-quality and accurate imaging data. However, collecting such data requires intensive and careful manual effort. One of the major challenges in imaging the Shoot Apical Meristem (SAM) of Arabidopsis thaliana, is that the deeper slices in the z-stack suffer from different perpetual quality related problems like poor contrast and blurring. These quality related issues often lead to disposal of the painstakingly collected data with little to no control on quality while collecting the data. Therefore, it becomes necessary to employ and design techniques that can enhance the images to make it more suitable for further analysis. In this paper, we propose a data-driven Deep Quantized Latent Representation (DQLR) methodology for high-quality image reconstruction in the Shoot Apical Meristem (SAM) of Arabidopsis thaliana. Our proposed framework utilizes multiple consecutive slices in the z-stack to learn a low dimensional latent space, quantize it and subsequently perform reconstruction using the quantized representation to obtain sharper images. Experiments on a publicly available dataset validate our methodology showing promising results. Our code is available at github.com/agupt013/enhancedRec.git.","PeriodicalId":329356,"journal":{"name":"2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops)","volume":"9 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2020-04-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"114627568","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 Anett For Solving Inverse Problems With Deep Learning 用深度学习求解逆问题的稀疏安妮特

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153362

D. Obmann, Linh V. Nguyen, Johannes Schwab, M. Haltmeier

引用次数: 7

Scan-Specific Accelerated Mri Reconstruction Using Recurrent Neural Networks In A Regularized Self-Consistent Framework 在正则化自洽框架中使用递归神经网络的扫描特异性加速Mri重建

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153389

S. A. Hosseini, Burhaneddin Yaman, Chi Zhang, K. Uğurbil, S. Moeller, M. Akçakaya

引用次数: 0

Oct Segmentation Using Convolutional Neural Network 卷积神经网络的Oct分割

2020 IEEE 17th International Symposium on Biomedical Imaging Workshops (ISBI Workshops) Pub Date : 2020-04-01 DOI: 10.1109/ISBIWorkshops50223.2020.9153418

Neetha George, C. Jiji

引用次数: 0