Paper Info
Title
Multi-Scale Residual Network With Two Channels Of Raw Ct Image And Its Differential Excitation Component For Emphysema Classification
Abstract
Automated tissue classification is an essential step for quantitative analysis and treatment of emphysema. Although many studies have been conducted in this area, there still remain two major challenges. First, different emphysematous tissue appears in different scales, which we call "inter-class variations". Second, the intensities of CT images acquired from different patients, scanners or scanning protocols may vary, which we call "intra-class variations". In this paper, we present a novel multi-scale residual network with two channels of raw CT image and its differential excitation component. We incorporate multi-scale information into our networks to address the challenge of inter-class variations. In addition to the conventional raw CT image, we use its differential excitation component as a pair of inputs to handle intra-class variations. Experimental results show that our approach has superior performance over the state-of-the-art methods, achieving a classification accuracy of 93.74% on our original emphysema database.
Year
DOI
Venue
2018
10.1007/978-3-030-00889-5_5
DEEP LEARNING IN MEDICAL IMAGE ANALYSIS AND MULTIMODAL LEARNING FOR CLINICAL DECISION SUPPORT, DLMIA 2018
Keywords
Field
DocType
Emphysema classification, Multi-scale, Differential excitation component
Residual,Pattern recognition,Computer science,Communication channel,Excitation,Artificial intelligence
Conference
Volume
ISSN
Citations 
11045
0302-9743
0
PageRank 
References 
Authors
0.34
0
9
Name
Order
Citations
PageRank
Liying Peng122.87
Lanfen Lin27824.70
Hongjie Hu3119.50
Huali Li421.52
Qingqing Chen563.86
Dan Wang610140.29
Xian-Hua Han71410.19
Yutaro Iwamoto81317.95
Yen-Wei Chen9720155.73