Alexandre Arnould

CLEAR-AF improves autofluorescence subtraction in multiplexed tissue imaging by using polar transformation and Gaussian Mixture Models, enhancing specificity, sensitivity, and reproducibility.

CONCLAVE improves cell annotation in cyclic multiplexed immunofluorescence data by using consensus clustering and validation, enhancing accuracy and reproducibility.

COLLAGE improves image alignment for multiplexed immunohistochemistry by breaking alignment into smaller puzzles, enabling parallel processing and reducing errors.

QUAL-IF-AI is a deep learning tool that detects and manages artifacts in multiplex immunofluorescence images, achieving high accuracy and IoU across common artifact types.