Paper Info
Title
A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA.
Abstract
The purpose of this study is to evaluate the feasibility of a novel feature generation, which is based on multiple deep neural networks (DNNs) with boosting, for computer-assisted detection (CADe). It is hard and time-consuming to optimize the hyperparameters for DNNs such as stacked denoising autoencoder (SdA). The proposed method allows using SdA based features without the burden of the hyperparameter setting. The proposed method was evaluated by an application for detecting cerebral aneurysms on magnetic resonance angiogram (MRA). A baseline CADe process included four components; scaling, candidate area limitation, candidate detection, and candidate classification. Proposed feature generation method was applied to extract the optimal features for candidate classification. Proposed method only required setting range of the hyperparameters for SdA. The optimal feature set was selected from a large quantity of SdA based features by multiple SdAs, each of which was trained using different hyperparameter set. The feature selection was operated through ada-boost ensemble learning method. Training of the baseline CADe process and proposed feature generation were operated with 200 MRA cases, and the evaluation was performed with 100 MRA cases. Proposed method successfully provided SdA based features just setting the range of some hyperparameters for SdA. The CADe process by using both previous voxel features and SdA based features had the best performance with 0.838 of an area under ROC curve and 0.312 of ANODE score. The results showed that proposed method was effective in the application for detecting cerebral aneurysms on MRA.
Year
DOI
Venue
2016
10.1117/12.2216832
Proceedings of SPIE
Keywords
Field
DocType
Computer assisted detection (CADe),magnetic resonance angiogram (MRA),cerebral aneurysm,deep neural network (DNN),stacked denoising autoencoder (SdA),and hyperparameter optimization
Voxel,Hyperparameter optimization,Computer vision,Feature selection,Pattern recognition,Hyperparameter,Computer-aided diagnosis,Artificial intelligence,Boosting (machine learning),Artificial neural network,Ensemble learning,Physics
Conference
Volume
ISSN
Citations 
9785
0277-786X
0
PageRank 
References 
Authors
0.34
0
7
Name
Order
Citations
PageRank
Mitsutaka Nemoto1468.42
Naoto Hayashi2206.38
Shouhei Hanaoka3267.56
Nomura, Y.4319.51
Soichiro Miki5156.44
Takeharu Yoshikawa6267.93
Kuni Ohtomo74511.32