Distilling Knowledge from Topological Representations for Pathological Complete Response Prediction

Shiyi Du, Qicheng Lao, Qingbo Kang, Yiyue Li, Zekun Jiang, Yanfeng Zhao, Kang Li

Abstract: In breast radiology, pathological Complete Response (pCR) predicts the treatment response after neoadjuvant chemotherapy, and therefore is a vital indicator for both personalized treatment and prognosis. Current prevailing approaches for pCR prediction either require complex feature engineering or employ sophisticated topological computation, which are not efficient while yielding limited performance boosts. In this paper, we present a simple yet effective technique implementing persistent homology to extract multi-dimensional topological representations from 3D data, making the computation much faster. To incorporate the extracted topological information, we then propose a novel approach to distill the extracted topological knowledge into deep neural networks with response-based knowledge distillation. Our experimental results quantitatively show that the proposed approach achieves superior performance by increasing the accuracy from previously 85.1% to 90.5% in the pCR prediction and reducing the topological computation time by about 66% on a public dataset for breast DCE-MRI images.