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Proceedings Paper

A primitive study of voxel feature generation by multiple stacked denoising autoencoders for detecting cerebral aneurysms on MRA
Author(s): Mitsutaka Nemoto; Naoto Hayashi; Shouhei Hanaoka; Yukihiro Nomura; Soichiro Miki; Takeharu Yoshikawa; Kuni Ohtomo
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Paper 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.

Paper Details

Date Published: 24 March 2016
PDF: 7 pages
Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 97852S (24 March 2016); doi: 10.1117/12.2216832
Show Author Affiliations
Mitsutaka Nemoto, The Univ. of Tokyo Hospital (Japan)
Naoto Hayashi, The Univ. of Tokyo Hospital (Japan)
Shouhei Hanaoka, The Univ. of Tokyo Hospital (Japan)
Yukihiro Nomura, The Univ. of Tokyo Hospital (Japan)
Soichiro Miki, The Univ. of Tokyo Hospital (Japan)
Takeharu Yoshikawa, The Univ. of Tokyo Hospital (Japan)
Kuni Ohtomo, The Univ. of Tokyo Hospital (Japan)

Published in SPIE Proceedings Vol. 9785:
Medical Imaging 2016: Computer-Aided Diagnosis
Georgia D. Tourassi; Samuel G. Armato, Editor(s)

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