FKDNuSeg: Flawless knowledge distillation for lightweight and fast nuclei instance segmentation and classification

Nuclei segmentation and classification is a fundamental task in the field of computational pathology. Existing approaches have demonstrated outstanding performance. However, they still suffer from the efficiency problem due to the extremely huge resolution of whole slide images and the complexity of the model, which greatly hinder their viability in real-world clinical scenarios. In this study, we propose a lightweight and fast nuclei segmentation and classification model, called FKDNuSeg. FKDNuSeg employs a knowledge distillation strategy with a multi-task learning architecture using ENet as the backbone. A flawless knowledge distillation method is designed to encourage the student model to learn the correct knowledge from the teacher model while preventing it being over influenced by flawed information, especially for the minority classes. An edge detection task enhanced with a curvature module is introduced to extract more precise nuclei edges and separate the attached or overlapping nuclei. FKDNuSeg is over 10× smaller than the state-of-the-art models with only 4.11M parameters. The proposed model achieves an inference time (patch-level) of 1.38 ms on an RTX 3090 GPU. Extensive experiments on two publicly available datasets CoNSeP and PanNuke demonstrate that FKDNuSeg significantly reduces the model complexity and improves the model efficiency while maintaining considerable performance.