Efficient skin lesion segmentation with boundary distillation

Zaifang Zhang; Boyang Lu

doi:10.1007/s11517-024-03095-y

Efficient skin lesion segmentation with boundary distillation

Med Biol Eng Comput. 2024 May 1. doi: 10.1007/s11517-024-03095-y. Online ahead of print.

Authors

Zaifang Zhang¹, Boyang Lu²

Affiliations

¹ School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China. zaifangzhang@shu.edu.cn.
² School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, 200444, China.

PMID: 38691269
DOI: 10.1007/s11517-024-03095-y

Abstract

Medical image segmentation models are commonly known for their complex structures, which often render them impractical for use on edge computing devices and compromising efficiency in the segmentation process. In light of this, the industry has proposed the adoption of knowledge distillation techniques. Nevertheless, the vast majority of existing knowledge distillation methods are focused on the classification tasks of skin diseases. Specifically, for the segmentation tasks of dermoscopy lesion images, these knowledge distillation methods fail to fully recognize the importance of features in the boundary regions of lesions within medical images, lacking boundary awareness for skin lesions. This paper introduces pioneering medical image knowledge distillation architecture. The aim of this method is to facilitate the efficient transfer of knowledge from existing complex medical image segmentation networks to a more simplified student network. Initially, a masked boundary feature (MBF) distillation module is designed. By applying random masking to the periphery of skin lesions, the MBF distillation module obliges the student network to reproduce the comprehensive features of the teacher network. This process, in turn, augments the representational capabilities of the student network. Building on the MBF distillation module, this paper employs a cascaded combination approach to integrate the MBF distillation module into a multi-head boundary feature (M²BF) distillation module, further strengthening the student network's feature learning ability and enhancing the overall image segmentation performance of the distillation model. This method has been experimentally validated on the public datasets ISIC-2016 and PH², with results showing significant performance improvements in the student network. Our findings highlight the practical utility of the lightweight network distilled using our approach, particularly in scenarios demanding high operational speed and minimal storage usage. This research offers promising prospects for practical applications in the realm of medical image segmentation.

Keywords: Bounder feature; Feature distillation; Knowledge distillation; Lightweight neural network; Medical image segmentation.