MuSARCyto : Multi‐Head Self‐Attention‐Based Representation Learning for Unsupervised Clustering of Cytometry Data

), which is an ensemble of prevalent cluster evaluation metrics. Extensive experimentation demonstrates the superior performance of MuSARCyto against the existing state-of-the-art cytometry clustering methods across six publicly available mass and flow cytometry datasets. The proposed DL achitectures are small and easily deployable for clinical settings. This work further suggests using DL methods for identifying meaningful clusters, particularly in the context of critical immunology applications.