Interpretable Multi-Instance Learning Enables Early Prediction of Key Molecular Alterations from Routine Flow Cytometry in Acute Myeloid Leukemia

arXiv:2609.18825 · cs.LG · Submitted 2026-09-16 · Read on arXiv

cs.LG

Submitted: 2026-09-16

Updated: 2026-09-16

Code: https://github.com/jonathan-legrand/cytometry-molecular-characterization

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• AUROC — 22× in this paper · explained in 2 episodes
• Acute Myeloid Leukemia — 13× in this paper
• Positive Predictive Value — 5× in this paper
• Interpretability — 4× in this paper · explained in 15 episodes
• Generalization — 3× in this paper · explained in 13 episodes
• Adaptation — 2× in this paper · explained in 1 episode
• Aggregation — 2× in this paper · explained in 1 episode
• Domain Adaptation — 2× in this paper · explained in 5 episodes
• Generalizability — 2× in this paper · explained in 6 episodes
• Pairwise Comparisons — 2× in this paper · explained in 2 episodes
• Accuracy — 1× in this paper · explained in 1 episode
• Computational overhead — 1× in this paper · explained in 2 episodes
• Diagnostic Tools — 1× in this paper · explained in 2 episodes
• Negative Predictive Value — 1× in this paper
• Predictive Modeling — 1× in this paper · explained in 2 episodes
• Pruning — 1× in this paper · explained in 4 episodes
• alpha — 1× in this paper · explained in 1 episode

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