dc.creator | Georgakopoulos S.V., Iakovidis D.K., Vasilakakis M., Plagianakos V.P., Koulaouzidis A. | en |
dc.date.accessioned | 2023-01-31T07:40:17Z | |
dc.date.available | 2023-01-31T07:40:17Z | |
dc.date.issued | 2016 | |
dc.identifier | 10.1109/IST.2016.7738279 | |
dc.identifier.isbn | 9781509018178 | |
dc.identifier.uri | http://hdl.handle.net/11615/72057 | |
dc.description.abstract | Graphic image annotations provide the necessary ground truth information for supervised machine learning in image-based computer-aided medical diagnosis. Performing such annotations is usually a time-consuming and cost-inefficient process requiring knowledge from domain experts. To cope with this problem we propose a novel weakly-supervised learning method based on a Convolutional Neural Network (CNN) architecture. The advantage of the proposed method over conventional supervised approaches is that only image-level semantic annotations are used in the training process, instead of pixel-level graphic annotations. This can drastically reduce the required annotation effort. Its advantage over the few state-of-the-art weakly-supervised CNN architectures is its simplicity. The performance of the proposed method is evaluated in the context of computer-aided detection of inflammatory gastrointestinal lesions in wireless capsule endoscopy videos. This is a broad category of lesions, for which early detection and treatment can be of vital importance. The results show that the proposed weakly-supervised learning method can be more effective than the conventional supervised learning, with an accuracy of 90%. © 2016 IEEE. | en |
dc.language.iso | en | en |
dc.source | IST 2016 - 2016 IEEE International Conference on Imaging Systems and Techniques, Proceedings | en |
dc.source.uri | https://www.scopus.com/inward/record.uri?eid=2-s2.0-85003881568&doi=10.1109%2fIST.2016.7738279&partnerID=40&md5=39942175f2b75776896e44b9f85d9017 | |
dc.subject | Artificial intelligence | en |
dc.subject | Computer aided diagnosis | en |
dc.subject | Convolution | en |
dc.subject | Diagnosis | en |
dc.subject | Endoscopy | en |
dc.subject | Image analysis | en |
dc.subject | Imaging systems | en |
dc.subject | Learning systems | en |
dc.subject | Medical imaging | en |
dc.subject | Network architecture | en |
dc.subject | Neural networks | en |
dc.subject | Semantics | en |
dc.subject | Supervised learning | en |
dc.subject | Computer aided detection | en |
dc.subject | Convolutional neural network | en |
dc.subject | Gastrointestinal lesions | en |
dc.subject | inflammatory lesions | en |
dc.subject | Lesion detection | en |
dc.subject | Supervised machine learning | en |
dc.subject | Weakly supervised learning | en |
dc.subject | Wireless capsule endoscopy | en |
dc.subject | Computer aided instruction | en |
dc.subject | Institute of Electrical and Electronics Engineers Inc. | en |
dc.title | Weakly-supervised Convolutional learning for detection of inflammatory gastrointestinal lesions | en |
dc.type | conferenceItem | en |