Abstract
In recent years, knowledge distillation for semantic segmentation has been extensively studied in order to obtain satisfactory performance while reducing computational costs. Compared with natural images, segmentation targets in medical images have fuzzy boundaries that are difficult to determine, but current knowledge distillation methods fail to explicitly transfer boundary information, resulting in poor boundary discrimination in compact models. Therefore, in this paper, we propose two knowledge distillation modules, namely boundary-guided deep supervision and output space boundary embeddng alignment, to explicitly transfer boundary information. The validity of our knowledge distillation approaches is demonstrated by extensive experiemnts on four public medical image data sets, namely, Montgomery County, CHAOS, GlaS and DRISHTI-GS.
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