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Large-scale Medical Image Processing

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Title
Large-scale Medical Image Processing
Author
Kim, Jisoo
Advisor
Chun, Se Young
Issue Date
2020-02
Publisher
Graduate School of UNIST
Abstract
Deep learning based approaches for vision motivated many researchers in Medical Image processing fields due to the powerful performances. Compare to the natural image data, the medical image data set commonly consumes huge memory with complex data structures. In addition, to demonstrate the large scale images for clinical purpose such as CT scans or pathological image data, it is commonly known to be difficult so that direct application of conventional deep models with typical GPU usage should be considered. For example, in the pathological data which is the image of microscope of human cells to classify tumor cells or not, the size of image slide is far larger than natural high resolution images while the field of view (FOV) that we are interested region is tiny. On the other hand, to handle the large scale of CT data which is using X-ray beams to visualize in-vivo hardness structures, due to the memory limitation of GPU device, the patch-wise method is suppressed to yield high performance and is disturbed to compute faster. Thus, in this paper, we investigate the how data balancing method effectively enhance the deep approach method when there is only unbalanced dataset. Furthermore, we propose the efficient memory utilization of multi-gpu method for deep learning with large scale CT images.
Description
Department of Electrical Engineering
URI
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EE_Theses_Master
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