@inproceedings{8026d0d453964f03add64d42ddf25252,
title = "Multi-phase liver lesions classification using relevant visual words based on mutual information",
abstract = "We present a novel method for automated diagnosis of liver lesions in multi-phase CT images. Our approach is a variant of the Bag-of-Visual-Words (BoVW) method. It improves the BoVW model by selecting the most relevant words to be used for the input representation using a mutual information based criterion. Additionally, we generate relevance maps to visualize and localize the decision of the automatic classification algorithm. We validated our algorithm on 85 multi-phase CT images of 4 categories: hemangiomas, Focal Nodular Hyper-plasia (FNH), Hepatic Cellular Carcinoma (HCC) and cholangiocarcinoma. The new algorithm suggested in this paper improves the classical BoVW method sensitivity by 7% and specificity by 3%. The shift from single-phase liver data to a multi-phase representation is shown to substantially improve classification results. Overall, the system presented reaches state-of-the-art classification results of 82.4% sensitivity and 92.7% specificity on the 4 category lesion data, a challenging clinical diagnosis task.",
keywords = "Liver lesions, automated diagnosis, classification, feature selection, mutual information, visual words",
author = "Idit Diamant and Jacob Goldberger and Eyal Klang and Michal Amitai and Hayit Greenspan",
note = "Publisher Copyright: {\textcopyright} 2015 IEEE.; 12th IEEE International Symposium on Biomedical Imaging, ISBI 2015 ; Conference date: 16-04-2015 Through 19-04-2015",
year = "2015",
month = jul,
day = "21",
doi = "10.1109/ISBI.2015.7163898",
language = "אנגלית",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "407--410",
booktitle = "2015 IEEE 12th International Symposium on Biomedical Imaging, ISBI 2015",
address = "ארצות הברית",
}
