@inproceedings{8c53630b27384d5a92bb10ee535efa0d,
title = "Training a neural network based on unreliable human annotation of medical images",
abstract = "Building classification models from clinical data often requires human experts for example labeling. However, it is difficult to obtain a perfect set of labels due to the complexity of the medical data and the large variability between experts. In this study we present a neural-network training strategy that is more robust to unreliable labeling by explicitly modeling the label noise as part of the network architecture. Our method is demonstrated on breast microcalcifications classification into benign and malignant categories, given multi-view mammograms. We show that the proposed training procedure outperforms standard training methods that ignore the existence of label noise.",
keywords = "Deep-learning, Mammography, Microcalcifications, Noisy-labels, Robust training",
author = "Yair Dgani and Hayit Greenspan and Jacob Goldberger",
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.2018.8363518",
language = "الإنجليزيّة",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "39--42",
booktitle = "2018 IEEE 15th International Symposium on Biomedical Imaging, ISBI 2018",
address = "الولايات المتّحدة",
}