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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