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Abstract #5041

3D Lesion Generation Model considering Anatomic Localization to Improve Object Detection in Limited Lacune Data

Daniel Kim1, Jae-Hun Lee1, Mohammed A. Al-masni2, Jun-ho Kim1, Yoonseok Choi1, Eun-Gyu Ha1, SunYoung Jung3, Young Noh4, and Dong-Hyun Kim1
1Department of Electrical and Electronic Engineering, Yonsei Univ., Seoul, Korea, Republic of, 2Department of Artificial Intelligence, Sejong Univ., Seoul, Korea, Republic of, 3Department of Biomedical Engineering, Yonsei Univ., Wonju, Korea, Republic of, 4Department of Neurology, Gachon Univ., Incheon, Korea, Republic of

Synopsis

Keywords: Machine Learning/Artificial Intelligence, Data Processing, AugmentationRecently, research on the detection of cerebral small vessel disease (CSVD) has been mainly implemented in two-stages (1st: candidate detection, 2nd: false-positive reduction). Previous studies presented the difficulty of collecting labeled data as a limitation. Here, we synthesized the lesion through 3D-DCGAN and insert it at different locations on the MR image considering anatomical localization and alpha blending to augment labeled data. Through this, the detecting architecture was simplified to a single-stage, and the precision and recall values were improved by an average of 0.2.

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