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bioRxiv · 10.1101/2025.04.23.650210

Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models

Cultrera di Montesano, S.·D'Ascenzo, D.·Raghavan, S.·Amini, A. P.·Winter, P.·Crawford, L.
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Abstract

Accurately annotating cell types is essential for extracting biological insight from single-cell RNA-seq data. Although cell types are naturally organized into hierarchical ontologies, most computational models do not explicitly incorporate this structure into their training objectives. We introduce a hierarchical cross-entropy loss that aligns model objectives with biological structure. Applied to architectures ranging from linear models to transformers, this simple modification significantly improves out-of-distribution performance (12-15%) without added computational cost.

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Cultrera di Montesano, S., D'Ascenzo, D., Raghavan, S., Amini, A. P., Winter, P., Crawford, L.. 2025-04-23. Hierarchical cross-entropy loss improves atlas-scale single-cell annotation models. https://doi.org/10.1101/2025.04.23.650210

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