Publisher URL:
Publisher DOI: 10.1007/978-3-658-25326-4_72
Title: Feasibility of Colon Cancer Detection in Confocal Laser Microscopy Images Using Convolution Neural Networks
Language: English
Authors: Gessert, Nils 
Wittig, Lukas 
Drömann, Daniel 
Keck, Tobias 
Schlaefer, Alexander 
Ellebrecht, David B. 
Keywords: Computer Science - Computer Vision and Pattern Recognition;Computer Science - Computer Vision and Pattern Recognition
Issue Date: 4-Dec-2018
Source: Informatik aktuell : 327-332 (2018)
Journal or Series Name: Informatik aktuell 
Abstract (english): 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.
ISBN: 978-365825325-7
ISSN: 1431-472X
Institute: Medizintechnische Systeme E-1 
Type: InProceedings (Aufsatz / Paper einer Konferenz etc.)
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