@inproceedings{c3162dc4511a4a4684362af6d2a20585,
title = "Anatomical data augmentation for CNN based pixel-wise classification",
abstract = "In this work we propose a method for anatomical data augmentation that is based on using slices of computed tomography (CT) examinations that are adjacent to labeled slices as another resource of labeled data for training the network. The extended labeled data is used to train a U-net network for a pixel-wise classification into different hepatic lesions and normal liver tissues. Our dataset contains CT examinations from 140 patients with 333 CT images annotated by an expert radiologist. We tested our approach and compared it to the conventional training process. Results indicate superiority of our method. Using the anatomical data augmentation we achieved an improvement of 3% in the success rate, 5% in the classification accuracy, and 4% in Dice.",
keywords = "Augmentation, CT, Liver, Semi-supervised learning",
author = "Eyal Klang and Amitai, {Michal Marianne} and Jacob Goldberger and Hayit Greenspan",
note = "Publisher Copyright: {\textcopyright} 2018 IEEE.; 15th IEEE International Symposium on Biomedical Imaging, ISBI 2018 ; Conference date: 04-04-2018 Through 07-04-2018",
year = "2018",
month = may,
day = "23",
doi = "https://doi.org/10.1109/ISBI.2018.8363762",
language = "الإنجليزيّة",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1096--1099",
booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",
address = "الولايات المتّحدة",
}