Interpretable unsupervised learning enables accurate clustering with high-throughput imaging flow cytometry

A primary challenge of high-throughput imaging flow cytometry (IFC) is to analyze the vast amount of imaging data, especially in applications where ground truth labels are unavailable or hard to obtain. We present an unsupervised deep embedding algorithm, the Deep Convolutional Autoencoder-based Clu...

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Detalles Bibliográficos
Autores principales: Zhang, Zunming, Chen, Xinyu, Tang, Rui, Zhu, Yuxuan, Guo, Han, Qu, Yunjia, Xie, Pengtao, Lian, Ian Y., Wang, Yingxiao, Lo, Yu-Hwa
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Article
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667244/
https://www.ncbi.nlm.nih.gov/pubmed/37996496
http://dx.doi.org/10.1038/s41598-023-46782-w

Internet

https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10667244/
https://www.ncbi.nlm.nih.gov/pubmed/37996496
http://dx.doi.org/10.1038/s41598-023-46782-w

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