TY - JOUR
T1 - Automated classification of elliptical cancer cells with stain-free holographic imaging and self-supervised learning
AU - Rehman, Abdur
AU - An, Hyunbin
AU - Park, Seonghwan
AU - Moon, Inkyu
N1 - Publisher Copyright:
© 2024 Elsevier Ltd
PY - 2024/7
Y1 - 2024/7
N2 - Image-based stain-free elliptical cancer cell classification is challenging, due to the inter-class morphological similarity. In this paper, we address the classification of different types of cancer cell lines (lung, breast, bladder, and skin) by utilizing self-supervised learning, and compare it with supervised learning based on convolutional neural network. Digital holography in a microscopic configuration was used to obtain stain-free quantitative phase images representing the intracellular content and morphology of cells. The performance of self-supervised learning in natural images shows promising results, and consistently closes the gap between self-supervised and supervised learning. The ability of self-supervised learning to effectively utilize unlabeled data for training is instrumental in the biomedical domain, where labeled data is scarce. Our goal is to study different self-supervised frameworks of biomedical holographic data, and determine how they can be utilized to advance liquid biopsy for the detection of cancer cells. After extensive experimentation, we conclude that self-supervised learning improves the classification performance on cancer cell datasets, and outperforms supervised learning when training data is limited, which is mostly the case in biomedical imaging.
AB - Image-based stain-free elliptical cancer cell classification is challenging, due to the inter-class morphological similarity. In this paper, we address the classification of different types of cancer cell lines (lung, breast, bladder, and skin) by utilizing self-supervised learning, and compare it with supervised learning based on convolutional neural network. Digital holography in a microscopic configuration was used to obtain stain-free quantitative phase images representing the intracellular content and morphology of cells. The performance of self-supervised learning in natural images shows promising results, and consistently closes the gap between self-supervised and supervised learning. The ability of self-supervised learning to effectively utilize unlabeled data for training is instrumental in the biomedical domain, where labeled data is scarce. Our goal is to study different self-supervised frameworks of biomedical holographic data, and determine how they can be utilized to advance liquid biopsy for the detection of cancer cells. After extensive experimentation, we conclude that self-supervised learning improves the classification performance on cancer cell datasets, and outperforms supervised learning when training data is limited, which is mostly the case in biomedical imaging.
KW - Classification of cancer cells
KW - Deep learning
KW - Holographic cell imaging
KW - Self-supervised learning
KW - Stain-free analysis of cancer cells
UR - http://www.scopus.com/inward/record.url?scp=85184137454&partnerID=8YFLogxK
U2 - 10.1016/j.optlastec.2024.110646
DO - 10.1016/j.optlastec.2024.110646
M3 - Article
AN - SCOPUS:85184137454
SN - 0030-3992
VL - 174
JO - Optics and Laser Technology
JF - Optics and Laser Technology
M1 - 110646
ER -