Ms. Nazanin Makkinejad Profile

Nazanin Makkinejad

at Illinois Institute of Technology

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Proceedings Article | 27 February 2018 Paper

Reduction in training time of a deep learning model in detection of lesions in CT

Nazanin Makkinejad, Nima Tajbakhsh, Amin Zarshenas, Ashfaq Khokhar, Kenji Suzuki

Proceedings Volume 10575, 105753V (2018) https://doi.org/10.1117/12.2293679

KEYWORDS: Medical imaging, Feature extraction

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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).

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