Deep neural network-based classification of spectrally encoded confocal microscopy images of breast cancer tissue Conference Paper
| Authors: | Nessaee, A.; Kose, K.; Brachtel, E. F.; Kang, D. |
| Title: | Deep neural network-based classification of spectrally encoded confocal microscopy images of breast cancer tissue |
| Conference Title: | Biomedical Optics Meeting Congress 2024: Microscopy Histopathology and Analytics |
| Abstract: | Spectrally Encoded Confocal Microscopy (SECM) previously demonstrated the ability to visualize cellular features of malignant breast tissues. In this paper, we developed a deep neural network-based method for automatically classifying SECM breast images. © 2024 The Author(s) |
| Keywords: | breast cancer; confocal microscopy; medical imaging; neural-networks; breast tissues; microscopy images; network-based; cellulars; cancer tissues; breast images |
| Journal Title | Proceedings of the Optica Biophotonics Congress: Biomedical Optics 2024 (Translational, Microscopy, OCT, OTS, BRAIN) |
| Conference Dates: | 2024 Apr 7-10 |
| Conference Location: | Fort Lauderdale, FL |
| ISBN: | 978-1-957171-34-0 |
| Publisher: | Optical Society of America |
| Date Published: | 2024-01-01 |
| Language: | English |
| PROVIDER: | scopus |
| DOI: | 10.1364/MICROSCOPY.2024.MM3A.6 |
| DOI/URL: | |
| Notes: | Conference Paper: MM3A.6 -- Located in the section "MM3A - Computational and Machine Learning Advances I" -- Part of Optica's Technical Digest Series -- Source: Scopus |
