2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)最新文献_第5页

Fully automated classification of mammograms using deep residual neural networks 使用深度残差神经网络的乳房x线照片全自动分类

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950526

Neeraj Dhungel, G. Carneiro, A. Bradley

引用次数: 74

Convolutional neural network pruning to accelerate membrane segmentation in electron microscopy 卷积神经网络剪枝加速电子显微镜膜分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950600

J. Roels, Jonas De Vylder, J. Aelterman, Y. Saeys, W. Philips

{"title":"Convolutional neural network pruning to accelerate membrane segmentation in electron microscopy","authors":"J. Roels, Jonas De Vylder, J. Aelterman, Y. Saeys, W. Philips","doi":"10.1109/ISBI.2017.7950600","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950600","url":null,"abstract":"Biological membranes are one of the most basic structures and regions of interest in cell biology. In the study of membranes, segment extraction is a well-known and difficult problem because of impeding noise, directional and thickness variability, etc. Recent advances in electron microscopy membrane segmentation are able to cope with such difficulties by training convolutional neural networks. However, because of the massive amount of features that have to be extracted while propagating forward, the practical usability diminishes, even with state-of-the-art GPU's. A significant part of these network features typically contains redundancy through correlation and sparsity. In this work, we propose a pruning method for convolutional neural networks that ensures the training loss increase is minimized. We show that the pruned networks, after retraining, are more efficient in terms of time and memory, without significantly affecting the network accuracy. This way, we manage to obtain real-time membrane segmentation performance, for our specific electron microscopy setup.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79407313","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: 6

High-order boltzmann machine-based unsupervised feature learning for multi-atlas segmentation 基于高阶玻尔兹曼机器的无监督特征学习多图谱分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950571

Liang Sun, Wei Shao, Daoqiang Zhang

{"title":"High-order boltzmann machine-based unsupervised feature learning for multi-atlas segmentation","authors":"Liang Sun, Wei Shao, Daoqiang Zhang","doi":"10.1109/ISBI.2017.7950571","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950571","url":null,"abstract":"Multi-atlas based label fusionmethods have been successfully used for medical image segmentation. In the field of brain region segmentation, multi-atlas based methods propagate labels from multiple atlases to target image by the similarity between patches in target image and atlases. Most of existing multi-atlas based methods usually use intensity feature, which is hard to capture high-order information in brain images. In light of this, in this paper, we endeavor to apply high-order restricted Boltzmann machines to represent brain images and use the learnt feature for brain region of interesting (ROIs) segmentation. Specifically, we firstly capture the covariance and the mean information from patches by high-order Boltzmann Machine. Then, we propagate the label by the similarity of the learnt high-order features. We validate our feature learning method on two well-known label fusion methods e.g., local-weighted voting (LWV) and non-local mean patch-based method (PBM). Experimental results on the NIREP dataset demonstrate that our method can improve the performance of both LWV and PBM by using the high-order features.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"79408773","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

Neurite reconstruction from time-lapse sequences using co-segmentation 基于共分割的延时序列神经突重建

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950549

S. Gulyanon, N. Sharifai, Michael D. Kim, A. Chiba, G. Tsechpenakis

引用次数: 1

FMRI data classification based on hybrid temporal and spatial sparse representation 基于时空混合稀疏表示的FMRI数据分类

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950674

Huan Liu, Mianzhi Zhang, Xintao Hu, Yudan Ren, Shu Zhang, Junwei Han, Lei Guo, Tianming Liu

{"title":"FMRI data classification based on hybrid temporal and spatial sparse representation","authors":"Huan Liu, Mianzhi Zhang, Xintao Hu, Yudan Ren, Shu Zhang, Junwei Han, Lei Guo, Tianming Liu","doi":"10.1109/ISBI.2017.7950674","DOIUrl":"https://doi.org/10.1109/ISBI.2017.7950674","url":null,"abstract":"Task-based functional magnetic resonance imaging (tfMRI) is widely used to localize brain regions or networks in response to various cognitive tasks. However, given two groups of tfMRI data acquired under distinct task paradigms, it is not clear whether there exist intrinsic inter-group differences in signal composition patterns, and if so, whether these differences could be used for data discrimination. The major challenges originate from the high dimensionality and low signal-to-noise ratio of fMRI data. In this paper, we proposed a novel framework using hybrid temporal and spatial sparse representation to tackle above challenges. We applied the proposed framework to the Human Connectome Project (HCP) tfMRI data. Our experimental results demonstrated that the task types of fMRI data can be successfully classified, achieving a 100% classification accuracy. We also showed that both task-related components and resting state networks (RSNs) can be reliably identified. Our study provides a novel data-driven approach to detecting discriminative inter-group differences in fMRI data based on signal composition patterns, and thus potentially can be used to control fMRI data quality and to infer biomarkers in brain disorders.","PeriodicalId":6547,"journal":{"name":"2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2017-04-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"78882092","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

Quad-edge active contours for biomedical image segmentation 生物医学图像分割的四边缘活动轮廓

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950715

D. G. Obando, Lauriane Rohfritsch, M. Faure, L. Danglot, V. Meas-Yedid, J. Olivo-Marin, A. Dufour

引用次数: 0

Case-control discrimination through effective brain connectivity 通过有效的大脑连接进行病例对照辨别

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950677

A. Crimi, Luca Dodero, Vittorio Murino, Diego Sona

引用次数: 15

Globally optimal breast mass segmentation from DCE-MRI using deep semantic segmentation as shape prior 基于深度语义分割作为形状先验的DCE-MRI整体优化乳腺肿块分割

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950525

Gabriel Maicas, G. Carneiro, A. Bradley

引用次数: 25

Age estimation from brain MRI images using deep learning 利用深度学习从脑MRI图像中估计年龄

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950650

Tzu-Wei Huang, Hwann-Tzong Chen, Ryuichi Fujimoto, Koichi Ito, Kai Wu, Kazunori Sato, Y. Taki, H. Fukuda, T. Aoki

引用次数: 48

Identifying the primary site of origin of MRI brain metastases from lung and breast cancer following a 2D radiomics approach 通过二维放射组学方法确定肺癌和乳腺癌MRI脑转移的原发部位

2017 IEEE 14th International Symposium on Biomedical Imaging (ISBI 2017) Pub Date : 2017-04-18 DOI: 10.1109/ISBI.2017.7950735

Rafael Ortiz-Ramón, A. Larroza, E. Arana, D. Moratal

引用次数: 4