@inproceedings{faeed8b9fb424be59081b39f4a0798a7,
title = "Weakly-Supervised Self-Training for Breast Cancer Localization",
abstract = "The use of deep learning methods has dramatically increased the state-of-the-art performance in image object localization. However, commonly used supervised learning methods require large training datasets with pixel-level or bounding box annotations. Obtaining such fine-grained annotations is extremely costly, especially in the medical imaging domain. In this work, we propose a novel weakly supervised method for breast cancer localization. The essential advantage of our approach is that the model only requires image-level labels and uses a self-training strategy to refine the predicted localization in a step-wise manner. We evaluated our approach on a large, clinically relevant mammogram dataset. The results show that our model significantly improves performance compared to other methods trained similarly.",
keywords = "convolutional neural network, mammography, Object localization",
author = "Gongbo Liang and Xiaoqin Wang and Yu Zhang and Nathan Jacobs",
note = "Publisher Copyright: {\textcopyright} 2020 IEEE.; 42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society, EMBC 2020 ; Conference date: 20-07-2020 Through 24-07-2020",
year = "2020",
month = jul,
doi = "10.1109/EMBC44109.2020.9176617",
language = "English",
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 = "1124--1127",
booktitle = "42nd Annual International Conferences of the IEEE Engineering in Medicine and Biology Society",
address = "United States",
}