3 papers
cs.CV2025
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.CV2021
Deep Unrolled Recovery in Sparse Biological Imaging
Yair Ben Sahel, John P. Bryan, Brian Cleary +2
Deep algorithm unrolling has emerged as a powerful model-based approach to develop deep architectures that combine the interpretability of iterative algorithms with the performance…
q-bio.OT2021
MITI Minimum Information guidelines for highly multiplexed tissue images
Denis Schapiro, Clarence Yapp, Artem Sokolov +36
The imminent release of tissue atlases combining multi-channel microscopy with single cell sequencing and other omics data from normal and diseased specimens creates an urgent need…