2 citations · 2 across the 7 of their papers we have counts for
4 papers · 1 filter
Massive Activations in Graph Neural Networks: Decoding Attention for Domain-Dependent Interpretability
Lorenzo Bini, Marco Sorbi, Stephane Marchand-Maillet
Graph Neural Networks (GNNs) have become increasingly popular for effectively modeling graph-structured data, and attention mechanisms have been pivotal in enabling these models to…
Injecting Hierarchical Biological Priors into Graph Neural Networks for Flow Cytometry Prediction
Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes +1
In the complex landscape of hematologic samples such as peripheral blood or bone marrow derived from flow cytometry (FC) data, cell-level prediction presents profound challenges. T…
FlowCyt: A Comparative Study of Deep Learning Approaches for Multi-Class Classification in Flow Cytometry Benchmarking
Lorenzo Bini, Fatemeh Nassajian Mojarrad, Margarita Liarou +2
This paper presents FlowCyt, the first comprehensive benchmark for multi-class single-cell classification in flow cytometry data. The dataset comprises bone marrow samples from 30…
Why Attention Graphs Are All We Need: Pioneering Hierarchical Classification of Hematologic Cell Populations with LeukoGraph
Fatemeh Nassajian Mojarrad, Lorenzo Bini, Thomas Matthes +1
In the complex landscape of hematologic samples such as peripheral blood or bone marrow, cell classification, delineating diverse populations into a hierarchical structure, present…