Unsupervised Deep Learning of Bright Field Images for Apoptotic Cell Classification
Zhuo Zheng, Beini Sun, Siqi He, Guanchen Wang, Chenyang Bi, Tongsheng Chen · Research Square · 2022
Abstract The classification of apoptotic and living cells is significant in drug screening and treating various diseases. Conventional supervised methods require a large amount of prelabelled data, which is often costly and consumes immense human resources in the biological field. In this study, unsupervised deep-learning algorithms were used to extract cell characteristics and classify cells. A model integrating a convolutional neural network and an auto-encoder network was utilised to extract cell characteristics, and a hybrid clustering approach was employed to obtain cell feature clustering results. Experiments on both public and private datasets revealed that the proposed unsupervised strategy performs well in cell categorisation. For instance, in the public dataset, our method obtained a precision of 96.72% on only 1000 unlabelled cells. To the best of our knowledge, this is the first time unsupervised deep learning has been applied to distinguish apoptosis and live cells with high accuracy.