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An Efficient Neural Network based on Early Compression of Sparse CT Slice Images
- Issue Date
- Aug-2021
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Keywords
- channel shuffle; CT Image; depthwise separable convolution; efficiency; lightweight deep learning; pointwise convolution; Thyroid-Associated ophthalmopathy
- Citation
- 2021 International Conference on Platform Technology and Service, PlatCon 2021 - Proceedings, pp 30 - 34
- Pages
- 5
- Journal Title
- 2021 International Conference on Platform Technology and Service, PlatCon 2021 - Proceedings
- Start Page
- 30
- End Page
- 34
- ISSN
- 0000-0000
- Abstract
- Recently, research on diagnosing diseases through artificial intelligence has been conducted in various medical fields, including Thyroid-Associated ophthalmopathy. We introduce a computationally efficient CNN architecture, which is optimized for CT images and designed especially for mobile devices with very limited computing power. The proposed architecture utilizes three operations, pointwise convolution, depth-wise separable convolution and channel shuffle, to reduce computation cost for handling a series of CT image slices for a patient. On CT images, the proposed model achieves ∼ 3.5 × actual speedup over ShuffleNet-v2 without degenerating prediction accuracy. © 2021 IEEE.
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Collections - College of Software > Department of Artificial Intelligence > 1. Journal Articles
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