Abstract
Biomedical instance segmentation is vulnerable to complicated instance morphology, resulting in over-merge and over-segmentation. Recent advanced methods apply convolutional neural networks to predict pixel embeddings to overcome this problem. However, these methods suffer from heavy computational burdens and massive storage. In this paper, we present the first knowledge distillation method tailored for biomedical instance segmentation to transfer the knowledge from a cumbersome teacher network to a lightweight student one. Different from existing distillation methods on other tasks, we consider three kinds of essential knowledge of the instance segmentation task, i.e., instance-level features, instance relationships in the feature space and pixel-level instance boundaries. Specifically, we devise two distillation schemes: (i) instance graph distillation that transfers the knowledge of instance-level features and instance relationships by the instance graphs built from embeddings of the teacher-student pair, respectively, and (ii) pixel affinity distillation that converts pixel embeddings into pixel affinities and explicitly transfers the structured knowledge of instance boundaries encoded in affinities. Experimental results on a 3D electron microscopy dataset (CREMI) and a 2D plant phenotype dataset (CVPPP) demonstrate that the student models trained through our distillation method use fewer than 1% parameters and less than 10% inference time while achieving promising performance compared with corresponding teacher models. Code is available at https://github.com/liuxy1103/BISKD.
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Acknowledgement
This work was supported in part by the National Key R &D Program of China under Grant 2017YFA0700800, the National Natural Science Foundation of China under Grant 62021001, the University Synergy Innovation Program of Anhui Province No. GXXT-2019-025, and Anhui Provincial Natural Science Foundation under grant No. 1908085QF256.
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Liu, X., Hu, B., Huang, W., Zhang, Y., Xiong, Z. (2022). Efficient Biomedical Instance Segmentation via Knowledge Distillation. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13434. Springer, Cham. https://doi.org/10.1007/978-3-031-16440-8_2
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