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Supervised deep learning performance is heavily tied to the availability of high-quality labels for training.
“Making deep neural networks robust to label noise: A loss correction approach,”
Giorgio Patrini, Alessandro Rozza, Aditya Krishna Menon, Richard Nock, and Lizhen Qu, · 1952
Earlier work this paper cites.
“Development of a digital image database for chest radiographs with and without a lung nodule: Receiver operating characteristic analysis of radiologists’ detection of pulmonary nodules,”
Junji Shiraishi, Shigehiko Katsuragawa, Junpei Ikezoe, Tsuneo Matsumoto, Takeshi Kobayashi, Ken-ichi Komatsu, Mitate Matsui, Hiroshi Fujita, Yoshie Kodera, and Kunio Doi, · 2000
Earlier work this paper cites.
“Segmentation of anatomical structures in chest radiographs using supervised methods: A comparative study on a public database,”
Bram Van Ginneken, Mikkel B Stegmann, and Marco Loog, · 2006
Earlier work this paper cites.
“Classification in the presence of label noise: A survey,”
Benoit Frenay and Michel Verleysen, · 2013
Earlier work this paper cites.
“U-Net: Convolutional networks for biomedical image segmentation,”
O. Ronneberger, P.Fischer, and T. Brox, · 2015
Earlier work this paper cites.
“Training deep neural-networks using a noise adaptation layer,”
Jacob Goldberger and Ehud Ben-Reuven, · 2017
Cited alongside, same era.
“Accurate lung segmentation via network-wise training of convolutional networks,”
Sangheum Hwang and Sunggyun Park, · 2017
Cited alongside, same era.
“Dimensionality-driven learning with noisy labels,”
Xingjun Ma, Yisen Wang, Michael E Houle, Shuo Zhou, Sarah M Erfani, Shu-Tao Xia, Sudanthi Wijewickrema, and James Bailey, · 2018
Cited alongside, same era.
“Co-teaching: Robust training of deep neural networks with extremely noisy labels,”
Bo Han, Quanming Yao, Xingrui Yu, Gang Niu, Miao Xu, Weihua Hu, Ivor Tsang, and Masashi Sugiyama, · 2018
Cited alongside, same era.
“Ce-Net: Context encoder network for 2D medical image segmentation,”
Zaiwang Gu, Jun Cheng, Huazhu Fu, Kang Zhou, Huaying Hao, Yitian Zhao, Tianyang Zhang, Shenghua Gao, and Jiang Liu, · 2019
Cited alongside, same era.
“Pick-and-learn: Automatic quality evaluation for noisy-labeled image segmentation,”
Haidong Zhu, Jialin Shi, and Ji Wu, · 2019
Later among the works it cites.
“Learning to segment skin lesions from noisy annotations,”
Zahra Mirikharaji, Yiqi Yan, and Ghassan Hamarneh, · 2019
Later among the works it cites.
“Non-local context encoder: Robust biomedical image segmentation against adversarial attacks,”
Xiang He, Sibei Yang, Guanbin Li, Haofeng Li, Huiyou Chang, and Yizhou Yu, · 2019
Later among the works it cites.
“DoubleU-Net: A deep convolutional neural network for medical image segmentation,”
Debesh Jha, Michael A Riegler, Dag Johansen, Pål Halvorsen, and Håvard D Johansen, · 2020
Later among the works it cites.
“Learning from noisy labels with deep neural networks: A survey,”
Hwanjun Song, Minseok Kim, Dongmin Park, and Jae-Gil Lee, · 2020
Later among the works it cites.
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