On the importance of local and global feature learning for automated measurable residual disease detection in flow cytometry data
This work addresses a domain-specific problem for the flow cytometry community, providing incremental improvements to existing methods.
The paper tackled automated measurable residual disease detection in flow cytometry data by evaluating deep learning methods and proposing two adaptations to the state-of-the-art model, resulting in superior performance on public datasets and improved generalization across laboratories.
This paper evaluates various deep learning methods for measurable residual disease (MRD) detection in flow cytometry (FCM) data, addressing questions regarding the benefits of modeling long-range dependencies, methods of obtaining global information, and the importance of learning local features. Based on our findings, we propose two adaptations to the current state-of-the-art (SOTA) model. Our contributions include an enhanced SOTA model, demonstrating superior performance on publicly available datasets and improved generalization across laboratories, as well as valuable insights for the FCM community, guiding future DL architecture designs for FCM data analysis. The code is available at \url{https://github.com/lisaweijler/flowNetworks}.