Why Attention Graphs Are All We Need: Pioneering Hierarchical
  Classification of Hematologic Cell Populations with LeukoGraph
Fatemeh Nassajian Mojarrad Lorenzo Bini Thomas Matthes Stéphane Marchand-Maillet
In the complex landscape of hematologic samples such as peripheral blood or
bone marrow, cell classification, delineating diverse populations into a
hierarchical structure, presents profound challenges. This study presents
LeukoGraph, a recently developed framework designed explicitly for this purpose
employing graph attention networks (GATs) to navigate hierarchical
classification (HC) complexities. Notably, LeukoGraph stands as a pioneering
effort, marking the application of graph neural networks (GNNs) for
hierarchical inference on graphs, accommodating up to one million nodes and
millions of edges, all derived from flow cytometry data. LeukoGraph intricately
addresses a classification paradigm where for example four different cell
populations undergo flat categorization, while a fifth diverges into two
distinct child branches, exemplifying the nuanced hierarchical structure
inherent in complex datasets. The technique is more general than this example.
A hallmark achievement of LeukoGraph is its F-score of 98%, significantly
outclassing prevailing state-of-the-art methodologies. Crucially, LeukoGraph's
prowess extends beyond theoretical innovation, showcasing remarkable precision
in predicting both flat and hierarchical cell types across flow cytometry
datasets from 30 distinct patients. This precision is further underscored by
LeukoGraph's ability to maintain a correct label ratio, despite the inherent
challenges posed by hierarchical classifications.