2 papers
cs.CV2026
CHAMMI-75: Pre-training multi-channel models with heterogeneous microscopy images
Vidit Agrawal, John Peters, Tyler N. Thompson +13
Quantifying cell morphology using images and machine learning has proven to be a powerful tool to study the response of cells to treatments. However, models used to quantify cellul…
cs.CV2025
Predicting Performance of Object Detection Models in Electron Microscopy Using Random Forests
Ni Li, Ryan Jacobs, Matthew Lynch +3
Quantifying prediction uncertainty when applying object detection models to new, unlabeled datasets is critical in applied machine learning. This study introduces an approach to es…