@inproceedings{e69055ea46904db6959fc5c26415cec0,
title = "Pancreatic cancer detection in whole slide images using noisy label annotations",
abstract = "We propose an approach to accurately predict regions of pancreatic cancer in whole-slide images (WSIs) by leveraging a relatively large, but noisy, dataset. We employ a noisy label classification (NLC) method (called the NLC model) that utilizes a small set of clean training samples and assigns the appropriate weights to training samples to deal with sample noise. The weights are assigned online so that the network loss approximates the loss for the clean samples. This method results in a 9.7\% performance improvement over the baseline non-NLC method (the Baseline-Noisy model). We use both methods in an ensemble setup to generate labels for a large training dataset to train a classifier. This classifier outperforms a classifier trained with manually annotated data by 2.94\%–3.74\% in terms of AUC for testing patches in WSIs.",
keywords = "Pancreas, Pancreatic cancer, Whole slide image",
author = "Han Le and Dimitris Samaras and Tahsin Kurc and Rajarsi Gupta and Kenneth Shroyer and Joel Saltz",
note = "Publisher Copyright: {\textcopyright} 2019, Springer Nature Switzerland AG.; 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019 ; Conference date: 13-10-2019 Through 17-10-2019",
year = "2019",
doi = "10.1007/978-3-030-32239-7\_60",
language = "English",
isbn = "9783030322380",
series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",
publisher = "Springer Science and Business Media Deutschland GmbH",
pages = "541--549",
editor = "Dinggang Shen and Pew-Thian Yap and Tianming Liu and Peters, \{Terry M.\} and Ali Khan and Staib, \{Lawrence H.\} and Caroline Essert and Sean Zhou",
booktitle = "Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 - 22nd International Conference, Proceedings",
}