|Publisher URL:||http://arxiv.org/abs/1812.01464v2||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
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.||URI:||http://hdl.handle.net/11420/2673||ISBN:||978-365825325-7||ISSN:||1431-472X||Institute:||Medizintechnische Systeme E-1||Type:||InProceedings (Aufsatz / Paper einer Konferenz etc.)|
|Appears in Collections:||Publications without fulltext|
Show full item record
checked on Jun 16, 2019
Items in TORE are protected by copyright, with all rights reserved, unless otherwise indicated.