CASIA OpenIR  > 中国科学院分子影像重点实验室
Multi-scale Convolutional Neural Networks for Lung Nodule Classification
Shen W(沈伟)1; Mu Zhou3; Feng Yang2; Caiyun Yang1; Jie Tian1; Tian J(田捷)
2015
会议名称Information Processing in Medical Imaging
会议录名称IPMI
会议日期2015-6
会议地点Sabhal Mor Ostaig College on the Isle of Skye, Scotland
摘要We investigate the problem of diagnostic lung nodule classifi cation using thoracic Computed Tomography (CT) screening. Unlike traditional studies primarily relying on nodule segmentation for regional analysis, we tackle a more challenging problem on directly modelling raw nodule patches without any prior de nition of nodule morphology. We propose a hierarchical learning framework--Multi-scale Convolutional Neural Networks (MCNN)--to capture nodule heterogeneity by extracting discriminative features from alternatingly stacked layers. In particular, to suffciently quantify nodule characteristics, our framework utilizes multi-scale nodule patches to learn a set of class-speci c features simultaneously by concatenating response neuron activations obtained at the last layer from each input scale. We evaluate the proposed method on CT images from Lung Image Database Consortium and Image Database Resource Initiative (LIDC-IDRI), where both lung nodule screening and nodule annotations are provided. Experimental results demonstrate the eff ectiveness of our method on classifying malignant and benign nodules without nodule segmentation.
关键词Lung Nodule Classification Computed Tomography Imaging Convolutional Neural Networks Computer-aided Diagnoses
收录类别EI
文献类型会议论文
条目标识符http://ir.ia.ac.cn/handle/173211/11699
专题中国科学院分子影像重点实验室
通讯作者Tian J(田捷)
作者单位1.Key Laboratory of Molecular Imaging of Chinese Academy of Sciences, Institute of Automation, Chinese Academy of Sciences
2.School of Computer and Information Technology, Beijing Jiaotong University, Beijing, China
3.Department of Computer Science and Engineering, University of South Florida, Tampa, United States
推荐引用方式
GB/T 7714
Shen W,Mu Zhou,Feng Yang,et al. Multi-scale Convolutional Neural Networks for Lung Nodule Classification[C],2015.
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