Arushi Gupta
@arushigupta.bsky.social
Incoming CS PhD at Stanford | Prev. at Caltech
We find that three leading single-cell models—PIFiA, Paired Cell Inpainting, and DeepLoc—are sensitive to background context.
Classification accuracy drops by up to 15.8% when background cell localization differs from the center cell, and improves when they match.
Classification accuracy drops by up to 15.8% when background cell localization differs from the center cell, and improves when they match.
July 4, 2025 at 9:50 PM
We find that three leading single-cell models—PIFiA, Paired Cell Inpainting, and DeepLoc—are sensitive to background context.
Classification accuracy drops by up to 15.8% when background cell localization differs from the center cell, and improves when they match.
Classification accuracy drops by up to 15.8% when background cell localization differs from the center cell, and improves when they match.
To assess if background alters model behavior, we evaluate five settings where the center cell is fixed while the background is either unaltered, swapped with backgrounds from the same or different protein localization classes, replaced with a cell-free background, or masked out.
July 4, 2025 at 9:50 PM
To assess if background alters model behavior, we evaluate five settings where the center cell is fixed while the background is either unaltered, swapped with backgrounds from the same or different protein localization classes, replaced with a cell-free background, or masked out.