Blind Source Separation In Dynamic Cell Imaging Using Non-Negative Matrix Factorization Applied To Breast Cancer Biopsies

D. Mandache (1) , E. Benoit a La Guillaume (2) , J.-C. Olivo-Marin (1) , V. Meas-Yedid (1)

1 BIA - Analyse d'images biologiques - Biological Image Analysis
2 LLTech SAS Paris

We propose a method to fully exploit the dynamic signal produced by a recently developed non-invasive imaging modality: Dynamic Cell Imaging based on Full Field Optical Coherence Tomography, towards fast extemporaneous tissue assessment. The non-negative matrix factorisation method is used in an interpretable and quantifiable fashion to extract the signals coming from different structures of breast tissue in order to characterize cancerous tissue.

Keywords: dynamic full field optical coherence tomography; data mining; non-negative matrix factorization

Domains: Image Processing [eess.IV]; Computer Vision and Pattern Recognition [cs.CV]; Machine Learning [cs.LG]; Urology and Nephrology; Cancer; Immunotherapy

D. Mandache, E. Benoit a La Guillaume, J.-C. Olivo-Marin, V. Meas-Yedid. Blind Source Separation In Dynamic Cell Imaging Using Non-Negative Matrix Factorization Applied To Breast Cancer Biopsies. 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Apr 2021, Nice, France. pp.1605-1608, ⟨10.1109/ISBI48211.2021.9434128⟩. ⟨pasteur-03721558⟩