Information-Distilled Generative Label-Free Morphological Profiling Encodes Cellular Heterogeneity.

ABSTRACT: Image-based cytometry faces challenges due to technical variations arising from different experimental batches and conditions, such as differences in instrument configurations or image acquisition protocols, impeding genuine biological interpretation of cell morphology. Existing solutions, often necessitating extensive pre-existing data knowledge or control samples across batches, have proved limited, especially with complex cell image data. To overcome this, "Cyto-Morphology Adversarial Distillation" (CytoMAD), a self-supervised multi-task learning strategy that distills biologically relevant cellular morphological information from batch variations, is introduced to enable integrated analysis across multiple data batches without complex data assumptions or extensive manual annotation. Uni

SUBMITTER: Lo MCK 

PROVIDER: S-EPMC11304271 | biostudies-literature | 2024 Aug

REPOSITORIES: biostudies-literature