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3 March 2022 Deep learning approach for metastatic cancer cell classification using live-cell imaging data
Seohyun Lee, Hyuno Kim, Hideo Higuchi, Masatoshi Ishikawa
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Proceedings Volume 11964, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XX; 1196404 (2022) https://doi.org/10.1117/12.2608017
Event: SPIE BiOS, 2022, San Francisco, California, United States
Abstract
The metastatic profile of the cancer cell is considered to be one of the most problematic characteristics from the pathogenic point of view. Because the metastatic cancer cells often show higher mobility compared to the non-metastatic cancer cells, distinguishing the metastatic cancer cell by their images can contain a clue to understanding the molecular process of the cellular metastasis-associated behaviors. In this study, we suggest a deep-learning approach to classify the metastatic cancer cells and non-metastatic cancer cells by their single-cell images acquired by phase-contrast microscopy.
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Seohyun Lee, Hyuno Kim, Hideo Higuchi, and Masatoshi Ishikawa "Deep learning approach for metastatic cancer cell classification using live-cell imaging data", Proc. SPIE 11964, Imaging, Manipulation, and Analysis of Biomolecules, Cells, and Tissues XX, 1196404 (3 March 2022); https://doi.org/10.1117/12.2608017
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KEYWORDS
Cancer
Image classification
Microscopy
Breast cancer
Imaging systems
Microscopes
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