MHKD: Multi-step Hybrid Knowledge Distillation for Low-resolution Whole Slide Images Glomerulus Detection

Xiangsen Zhang, Longfei Han, Chenchu Xu, Zhaohui Zheng, Jin Ding, Xianghui Fu, Dingwen Zhang, Junwei Han

Keywords: glomerulus detection, hybrid knowledge distillation, low-resolution pathology image, multi-step training strategy

Abstract:

Glomerulus detection is a critical component of renal histopathology assessment, essential for diagnosing glomerulonephritis.
To mitigate the increasing workload on pathologists, AI-assisted diagnostic methods based on high-resolution digital pathology whole slide images have been developed. However, these current AI-assisted approaches are limited to high-resolution whole slide images, necessitating expensive digital scanner equipment, high image storage costs, and significant computational complexity.
To address this limitation, this paper pioneers a method for facilitating glomerulus detection in low-resolution human kidney pathology images.
Specifically, we propose a novel multi-step hybrid knowledge distillation method.
Our method distills both the global features and the semantic information through a hybrid knowledge distillation strategy that integrates offline and online knowledge distillation, where the information from high-resolution pathological images is successively transferred to student model from the global features in the shallow network layers to the semantic information of the back-end through a multi-step training strategy.
Experimental results on two datasets show that the proposed method achieves effective detection outcomes for low-resolution kidney pathology images. Compared to other state-of-the-art detection techniques, our method achieves an $AP_{0.5:0.95}$ improvement of 23.1\% on the private LN dataset and 15.9\% on the public HUBMAP dataset.

Track: 4. AI-based clinical decision support systems

Registration Id: H3NTNJ5BK76

Submission Number: 267