Knowledge transfer to enhance the performance of deep learning models for automated classification of B cell neoplasms
Nanditha Mallesh, Max Zhao, Lisa Meintker, Alexander Höllein, F. Elsner, Hannes Lüling, Torsten Haferlach, Wolfgang Kern, Jörg Westermann, Peter Brossart, Stefan W. Krause, Peter Krawitz
Abstract
Multi-parameter flow cytometry (MFC) is a cornerstone in clinical decision making for leukemia and lymphoma. MFC data analysis requires manual gating of cell populations, which is time-consuming, subjective, and often limited to a two-dimensional space. In recent years, deep learning models have been successfully used to analyze data in high-dimensional space and are highly accurate. However, AI models used for disease classification with MFC data are limited to the panel they were trained on. Thus, a key challenge in deploying AI into routine diagnostics is the robustness and adaptability of such models. This study demonstrates how transfer learning can be applied to boost the performance of models with smaller datasets acquired with different MFC panels. We trained models for four additional datasets by transferring the features learned from our base model. Our workflow increased the model's overall performance and, more prominently, improved the learning rate for small training sizes.