@article {69349,
	title = {Transformers do not outperform Cellpose},
	journal = {bioRxiv},
	year = {2024},
	month = {2024 Apr 7},
	abstract = {<p>In a recent publication, Ma et al [1] claim that a transformer-based cellular segmentation method called Mediar [2] {\textemdash} which won a Neurips challenge {\textemdash} outperforms Cellpose [3] (0.897 vs 0.543 median F1 score). Here we show that this result was obtained by artificially impairing Cellpose in multiple ways. When we removed these impairments, Cellpose outperformed Mediar (0.861 vs 0.826 median F1 score on the updated test set). To further investigate the performance of transformers for cellular segmentation, we replaced the Cellpose backbone with a transformer. The transformer-Cellpose model also did not outperform the standard Cellpose (0.848 median F1 test score). Our results suggest that transformers do not advance the state-of-the-art in cellular segmentation.</p>
},
	doi = {10.1101/2024.04.06.587952},
	url = {https://www.biorxiv.org/content/early/2024/04/07/2024.04.06.587952},
	author = {Carsen Stringer and Marius Pachitariu}
}
