Automatic Embedding Interventions for the Classification of Hematopoietic Cells
Philipp Gräbel, Julian Thull, Martina Margrit Crysandt, Barbara M. Klinkhammer, Peter Boor, Tim H. Brümmendorf, Dorit Merhof · 2022 26th International Conference on Pattern Recognition (ICPR) · 2022
The classification of hematopoietic cells is the most essential step in automating the analysis of human bone marrow samples. However, the complex structure of cell classes as well as class imbalance make this a challenging task, even for neural networks. Based on projective latent interventions, we propose automatic interventions that iteratively update a learned embedding with suitable transformations that shift different cell types apart and contract samples of the same type together. We present different ways of applying these: either directly on a higher-dimensional embedding or in a parametric version in two dimensions. We analyze the hyper-parameters and evaluate the proposed approach on a challenging dataset of hematopoietic cells. The results show an improvement of up to 3 percentage points for the classification F-score.