Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks

Start Page
327
End Page
332
Citation
Workshop on Bildverarbeitung fur die Medizin, 2019: 327-332
Contribution to Conference
Workshop on Bildverarbeitung fur die Medizin, 2019  
Publisher DOI
10.1007/978-3-658-25326-4_72
Scopus ID
2-s2.0-85065090790
Publisher
Springer Vieweg
ISBN
978-3-658-25326-4
978-3-658-25325-7
Histological evaluation of tissue samples is a typical approach to identify colorectal cancer metastases in the peritoneum. For immediate assessment, reliable and real-time in-vivo imaging would be required. For example, intraoperative confocal laser microscopy has been shown to be suitable for distinguishing organs and also malignant and benign tissue. So far, the analysis is done by human experts. We investigate the feasibility of automatic colon cancer classification from confocal laser microscopy images using deep learning models. We overcome very small dataset sizes through transfer learning with state-of-the-art architectures. We achieve an accuracy of 89.1% for cancer detection in the peritoneum which indicates viability as an intraoperative decision support system.
Subjects
Computer Science - Computer Vision and Pattern Recognition
DDC Class
600: Technology