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  • ÀúÀÚYusuke Ozaki, Hidenao Yamada, Hirotoshi Kikuchi, Amane Hirotsu,Tomohiro Murakami,¿Ü Àú
  • ÃâÆÇ»ç¾ÆÁø
  • ÃâÆÇÀÏ2020-07-12
  • µî·ÏÀÏ2020-12-21
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It is demonstrated that cells can be classified by pattern recognition of the
subcellular structure of non-stained live cells, and the pattern recognition was
performed by machine learning. Human white blood cells and five types of cancer
cell lines were imaged by quantitative phase microscopy, which provides
morphological information without staining quantitatively in terms of optical
thickness of cells. Subcellular features were then extracted from the obtained
images as training data sets for the machine learning. The built classifier
successfully classified WBCs from cell lines (area under ROC curve = 0.996). This
label-free, noncytotoxic cell classification based on the subcellular structure of
QPM images has the potential to serve as an automated diagnosis of single cells.

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Á¦ 2Æí : ¿¬±¸³í¹®
Label-free classification of cells based on supervised machine learning
of subcellular structures

1. Introduction 51
2. Materials and methods 53
3. Subcellular features of single cells by HOG descriptor 56
4. Collection and processing of blood samples 58
5. Results 59
6. Interpretation of mechanism of classifications of cells 61
7. Discussion 64
8. References 68

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