2021
DOI: 10.1007/978-3-030-86261-9_8
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Optimizing Medical Image Classification Models for Edge Devices

Abstract: Machine learning algorithms for medical diagnostics often require resourceintensive environments to run, such as expensive cloud servers or high-end GPUs, making these models impractical for use in the field. We investigate the use of model quantization and GPU-acceleration for chest X-ray classification on edge devices. We employ 3 types of quantization (dynamic range, float-16, and full int8) which we tested on models trained on the Chest-XRay14 Dataset. We achieved a 2-4x reduction in model size, offset by … Show more

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Cited by 3 publications
(5 citation statements)
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“…Previous work exploring edge computing for breast cancer assessment using mammography image data could not be found. However, there are few papers describing the study of enabling technologies such as model optimization and hardware acceleration [10] and a blockchain-enabled learning model [11] with application examples in the medical imaging domain. Algorithms with low computational cost for medical image analysis have also been proposed [12].…”
Section: Ai-based Medical Image Assessmentmentioning
confidence: 99%
See 1 more Smart Citation
“…Previous work exploring edge computing for breast cancer assessment using mammography image data could not be found. However, there are few papers describing the study of enabling technologies such as model optimization and hardware acceleration [10] and a blockchain-enabled learning model [11] with application examples in the medical imaging domain. Algorithms with low computational cost for medical image analysis have also been proposed [12].…”
Section: Ai-based Medical Image Assessmentmentioning
confidence: 99%
“…The traditionally utilized, non-iterative Feldkamp-Davis-Kress (FDK) algorithm was used for reconstruction. The reconstructed slices were 1012-by-612 pixels in size, and the number of slices was modified (10,50,100,200) to address the influence of increasing reconstruction volume on the total computation time. Thus, the reconstructed volumes were 1012-by-612-by-…”
Section: Use-case 1: Image Reconstruction In Volumetric Cbctmentioning
confidence: 99%
“…The data loading and preprocessing pipeline is optimized to reduce the training time. The author introduces quantization-aware training to maintain the trade-off between model size and inference speed [43]. Quantization-aware training optimizes the proposed model using weight and activation redundancy [43].…”
Section: Lc Detection Modelmentioning
confidence: 99%
“…The author introduces quantization-aware training to maintain the trade-off between model size and inference speed [43]. Quantization-aware training optimizes the proposed model using weight and activation redundancy [43]. It allows the model to be lowered to a quarter of their original size and memory footprint.…”
Section: Lc Detection Modelmentioning
confidence: 99%
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