2024-01-23-haslum24a.md

title	abstract	layout	series	publisher	issn	id	month	tex_title	firstpage	lastpage	page	order	cycles	bibtex_author	author	date	address	container-title	volume	genre	issued	pdf	extras
Metadata-guided Consistency Learning for High Content Images	High content imaging assays can capture rich phenotypic response data for large sets of compound treatments, aiding in the characterization and discovery of novel drugs. However, extracting representative features from high content images that can capture subtle nuances in phenotypes remains challenging. The lack of high-quality labels makes it difficult to achieve satisfactory results with supervised deep learning. Self-Supervised learning methods have shown great success on natural images, and offer an attractive alternative also to microscopy images. However, we find that self-supervised learning techniques underperform on high content imaging assays. One challenge is the undesirable domain shifts present in the data known as batch effects, which may be caused by biological noise or uncontrolled experimental conditions. To this end, we introduce Cross-Domain Consistency Learning (CDCL), a novel approach that is able to learn in the presence of batch effects. CDCL enforces the learning of biological similarities while disregarding undesirable batch-specific signals, which leads to more useful and versatile representations. These features are organised according to their morphological changes and are more useful for downstream tasks - such as distinguishing treatments and mechanism of action.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	haslum24a	0	Metadata-guided Consistency Learning for High Content Images	918	936	918-936	918	false	Haslum, Johan Fredin and Matsoukas, Christos and Leuchowius, Karl-Johan and M\"ullers, Erik and Smith, Kevin	given family Johan Fredin Haslum given family Christos Matsoukas given family Karl-Johan Leuchowius given family Erik Müllers given family Kevin Smith	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/haslum24a/haslum24a.pdf