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Proceedings Paper

Reduction in training time of a deep learning model in detection of lesions in CT
Author(s): Nazanin Makkinejad; Nima Tajbakhsh; Amin Zarshenas; Ashfaq Khokhar; Kenji Suzuki
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Paper Abstract

Deep learning (DL) emerged as a powerful tool for object detection and classification in medical images. Building a well-performing DL model, however, requires a huge number of images for training, and it takes days to train a DL model even on a cutting edge high-performance computing platform. This study is aimed at developing a method for selecting a “small” number of representative samples from a large collection of training samples to train a DL model for the could be used to detect polyps in CT colonography (CTC), without compromising the classification performance. Our proposed method for representative sample selection (RSS) consists of a K-means clustering algorithm. For the performance evaluation, we applied the proposed method to select samples for the training of a massive training artificial neural network based DL model, to be used for the classification of polyps and non-polyps in CTC. Our results show that the proposed method reduce the training time by a factor of 15, while maintaining the classification performance equivalent to the model trained using the full training set. We compare the performance using area under the receiveroperating- characteristic curve (AUC).

Paper Details

Date Published: 27 February 2018
PDF: 11 pages
Proc. SPIE 10575, Medical Imaging 2018: Computer-Aided Diagnosis, 105753V (27 February 2018); doi: 10.1117/12.2293679
Show Author Affiliations
Nazanin Makkinejad, Illinois Institute of Technology (United States)
Nima Tajbakhsh, Illinois Institute of Technology (United States)
Amin Zarshenas, Illinois Institute of Technology (United States)
Ashfaq Khokhar, Iowa State Univ. (United States)
Kenji Suzuki, Illinois Institute of Technology (United States)

Published in SPIE Proceedings Vol. 10575:
Medical Imaging 2018: Computer-Aided Diagnosis
Nicholas Petrick; Kensaku Mori, Editor(s)

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