A Knowledge-Reserved Distillation with Complementary Transfer for Automated FC-based Classification Across Hematological Malignancies.

Abstract

Acute leukemia often comes with life-threatening prognosis outcome and remains a critical clinical issue today. The implementation of measurable residual disease (MRD) using flow cytometry (FC) is highly effective but the interpretation is time-consuming and suffers from physician idiosyncrasy. Recent machine learning algorithms have been proposed to automatically classify acute leukemia samples with and without MRD to address this clinical need. However, most prior works either validate only on a small data cohort or focus on one specific type of leukemia which lacks generalization. In this work, we propose a transfer learning approach in performing automatic MRD classification that takes advantage of a large scale acute myeloid leukemia (AML) database to facilitate better learning on a small cohort of acute lymphoblastic leukemia (ALL). Specifically, we develop a knowledge-reserved distilled AML pre-trained network with ALL complementary learning to enhance the ALL MRD classification. Our framework achieves 84.5% averaged AUC which shows its transferability across acute leukemia, and our further analysis reveals that younger and elder ALL patient samples benefit more from using the pre-trained AML model.