@inproceedings{c92167392d434a9c98798cd41e65ddb4,
title = "Robust learning at noisy labeled medical images: Applied to skin lesion classification",
abstract = "Deep neural networks (DNNs) have achieved great success in a wide variety of medical image analysis tasks. However, these achievements indispensably rely on the accurately-annotated datasets. If with the noisy-labeled images, the training procedure will immediately encounter difficulties, leading to a suboptimal classifier. This problem is even more crucial in the medical field, given that the annotation quality requires great expertise. In this paper, we propose an effective iterative learning framework for noisy-labeled medical image classification, to combat the lacking of high quality annotated medical data. Specifically, an online uncertainty sample mining method is proposed to eliminate the disturbance from noisy-labeled images. Next, we design a sample re-weighting strategy to preserve the usefulness of correctly-labeled hard samples. Our proposed method is validated on skin lesion classification task, and achieved very promising results.",
keywords = "Melanoma, Noisy-labels, Robust learning, Uncertainty, Weighted loss",
author = "Cheng Xue and Qi Dou and Xueying Shi and Hao Chen and Heng, \{Pheng Ann\}",
note = "Funding Information: This project is funded by Hong Kong Innovation and Technology Commission, under ITSP Tier 2 Scheme (Project No. ITS/426/17FP), and ITSP Tier 3 Scheme ( Project No. ITS/041/16) Publisher Copyright: {\textcopyright} 2019 IEEE.; 16th IEEE International Symposium on Biomedical Imaging, ISBI 2019 ; Conference date: 08-04-2019 Through 11-04-2019",
year = "2019",
month = apr,
doi = "10.1109/ISBI.2019.8759203",
language = "English",
series = "Proceedings - International Symposium on Biomedical Imaging",
publisher = "IEEE Computer Society",
pages = "1280--1283",
booktitle = "ISBI 2019 - 2019 IEEE International Symposium on Biomedical Imaging",
address = "United States",
}