Blind Source Separation In Dynamic Cell Imaging Using Non-Negative Matrix Factorization Applied To Breast Cancer Biopsies
- Resource Type
- Authors
- Jean-Christophe Olivo-Marin; Diana Mandache; E. Benoit a la Guillaume; Vannary Meas-Yedid
- Source
- 2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI)
2021 IEEE 18th International Symposium on Biomedical Imaging (ISBI), Apr 2021, Nice, France. pp.1605-1608, ⟨10.1109/ISBI48211.2021.9434128⟩
ISBI
- Subject
- Computer science
[SDV.CAN]Life Sciences [q-bio]/Cancer
01 natural sciences
Signal
Blind signal separation
[SDV.MHEP.UN]Life Sciences [q-bio]/Human health and pathology/Urology and Nephrology
030218 nuclear medicine & medical imaging
Non-negative matrix factorization
010309 optics
non-negative matrix factorization
03 medical and health sciences
0302 clinical medicine
Breast cancer
Optical coherence tomography
[INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG]
0103 physical sciences
medicine
Coherence (signal processing)
Modality (human–computer interaction)
Breast tissue
medicine.diagnostic_test
business.industry
[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV]
Pattern recognition
data mining
[SDV.IMM.IMM]Life Sciences [q-bio]/Immunology/Immunotherapy
medicine.disease
dynamic full field optical coherence tomography
[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV]
Artificial intelligence
business
- Language
- English
International audience; 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.