2024-01-23-ji24a.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
CP2Image: Generating high-quality single-cell images using CellProfiler representations	Single-cell high-throughput microscopy images contain key biological information underlying normal and pathological cellular processes. Image-based analysis and profiling are powerful and promising for extracting this information but are made difficult due to substantial complexity and heterogeneity in cellular phenotype. Hand-crafted methods and machine learning models are popular ways to extract cell image information. Representations extracted via machine learning models, which often exhibit good reconstruction performance, lack biological interpretability. Hand-crafted representations, on the contrary, have clear biological meanings and thus are interpretable. Whether these hand-crafted representations can also generate realistic images is not clear. In this paper, we propose a CellProfiler to image (CP2Image) model that can directly generate realistic cell images from CellProfiler representations. We also demonstrate most biological information encoded in the CellProfiler representations is well-preserved in the generating process. This is the first time hand-crafted representations be shown to have generative ability and provide researchers with an intuitive way for their further analysis.	inproceedings	Proceedings of Machine Learning Research	PMLR	2640-3498	ji24a	0	CP2Image: Generating high-quality single-cell images using CellProfiler representations	274	285	274-285	274	false	Ji, Yanni and Cutiongco, Marie and Jensen, Bj\orn Sand and Yuan, Ke	given family Yanni Ji given family Marie Cutiongco given family Bj\orn Sand Jensen given family Ke Yuan	2024-01-23		Medical Imaging with Deep Learning	227	inproceedings	date-parts 2024 1 23	https://proceedings.mlr.press/v227/ji24a/ji24a.pdf