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

Autor:	Gessert, Nils, Wittig, Lukas, Drömann, Daniel, Keck, Tobias, Schlaefer, Alexander, Ellebrecht, David B.
Rok vydání:	2018
Předmět:	Computer Science - Computer Vision and Pattern Recognition
Druh dokumentu:	Working Paper
DOI:	10.1007/978-3-658-25326-4_72
Popis:	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. Comment: Accepted at BVM Workshop 2019
Databáze:	arXiv
Externí odkaz:	http://arxiv.org/abs/1812.01464 Zobrazit plný text záznamu View this record from Arxiv