3 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
ChA-MAEViT: Unifying Channel-Aware Masked Autoencoders and Multi-Channel Vision Transformers for Improved Cross-Channel Learning
Chau Pham, Juan C. Caicedo, Bryan A. Plummer
Prior work using Masked Autoencoders (MAEs) typically relies on random patch masking based on the assumption that images have significant redundancies across different channels, al…
cs.CV2024
Enhancing Feature Diversity Boosts Channel-Adaptive Vision Transformers
Chau Pham, Bryan A. Plummer
Multi-Channel Imaging (MCI) contains an array of challenges for encoding useful feature representations not present in traditional images. For example, images from two different sa…