@inproceedings{69e0c15faa4d452ab06daab0da107303,
title = "Improving CNN Training using Disentanglement for Liver Lesion Classification in CT",
abstract = "Training data is the key component in designing algorithms for medical image analysis and in many cases it is the main bottleneck in achieving good results. Recent progress in image generation has enabled the training of neural network based solutions using synthetic data. A key factor in the generation of new samples is controlling the important appearance features and potentially being able to generate a new sample of a specific class with different variants. In this work we suggest the synthesis of new data by mixing the class specified and unspecified representation of different factors in the training data which are separated using a disentanglement based scheme. Our experiments on liver lesion classification in CT show an average improvement of 7.4% in accuracy over the baseline training scheme.",
keywords = "Disentanglement, Liver lesions, Medical, Synthesis",
author = "Avi Ben-Cohen and Roey Mechrez and Noa Yedidia and Hayit Greenspan",
note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019 ; Conference date: 23-07-2019 Through 27-07-2019",
year = "2019",
month = jul,
doi = "10.1109/EMBC.2019.8857465",
language = "אנגלית",
series = "Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBS",
publisher = "Institute of Electrical and Electronics Engineers Inc.",
pages = "886--889",
booktitle = "2019 41st Annual International Conference of the IEEE Engineering in Medicine and Biology Society, EMBC 2019",
address = "ארצות הברית",
}