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Labeling training datasets has become a key barrier to building medical machine learning models.
Maximum likelihood estimation of observer error-rates using the em algorithm
Dawid, A. P. & Skene, A. M · 1979
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Comparing the Areas under Two or More Correlated Receiver Operating Characteristic Curves: A Nonparametric Approach
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Automatic acquisition of hyponyms from large text corpora
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Combining labeled and unlabeled data with co-training
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Constructing biological knowledge bases by extracting information from text sources
Craven, M. & Kumlien, J · 1999
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Web-scale information extraction in KnowItAll
Etzioni, O. et al · 2004
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Time is brain–quantified
Saver, J. L · 2006
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Distant supervision for relation extraction without labeled data
Mintz, M., Bills, S., Snow, R. & Jurafsky, D · 2009
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ImageNet: A large-scale hierarchical image database
Jia Deng et al · 2009
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Who moderates the moderators?: Crowdsourcing abuse detection in user-generated content
Ghosh, A., Kale, S. & McAfee, P · 2011
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Learning to label aerial images from noisy data
Mnih, V. & Hinton, G. E · 2012
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Aggregating crowdsourced binary ratings
Dalvi, N., Dasgupta, A., Kumar, R. & Rastogi, V · 2013
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Measuring and managing radiologist workload: Measuring radiologist reporting times using data from a Radiology Information System
Cowan, I. A., MacDonald, S. L. & Floyd, R. A · 2013
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Using anchors to estimate clinical state without labeled data
Halpern, Y., Choi, Y., Horng, S. & Sontag, D · 2014
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Large-scale extraction of gene interactions from full-text literature using deepdive
Mallory, E. K., Zhang, C., Ré, C. & Altman, R. B · 2015
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Deep multiple instance learning for image classification and auto-annotation
Wu, J., Yu, Y., Huang, C. & Yu, K · 2015
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Going deeper with convolutions
Szegedy, C. et al · 2015
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Predicting non-small cell lung cancer prognosis by fully automated microscopic pathology image features
Yu, K.-H. et al · 2016
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Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs
Gulshan, V. et al · 2016
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Deep residual learning for image recognition
He, K., Zhang, X., Ren, S. & Sun, J · 2016
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Tensorflow: a system for large-scale machine learning
Abadi, M. et al · 2016
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Learning statistical models of phenotypes using noisy labeled training data
Agarwal, V. et al · 2016
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MURA: Large dataset for abnormality detection in musculoskeletal radiographs
Rajpurkar, P. et al · 2017
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Densely connected convolutional networks
Huang, G., Liu, Z., Van Der Maaten, L. & Weinberger, K. Q · 2017
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A review of feature extraction for EEG epileptic seizure detection and classification
Boubchir, L., Daachi, B. & Pangracious, V · 2017
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Deep learning based tissue analysis predicts outcome in colorectal cancer
Bychkov, D. et al · 2018
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Automated deep-neural-network surveillance of cranial images for acute neurologic events
Titano, J. J. et al · 2018
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An explainable deep-learning algorithm for the detection of acute intracranial haemorrhage from small datasets
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Data programming: Creating large training sets, quickly
Ratner, A. J., Sa, C. M. D., Wu, S., Selsam, D. & Ré, C · 2016
Cited alongside, same era.
The Temple University Hospital EEG data corpus
Obeid, I. & Picone, J · 2016
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Dermatologist-level classification of skin cancer with deep neural networks
Esteva, A. et al · 2017
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Automatic differentiation in PyTorch
Paszke, A. et al · 2017
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Position-aware attention and supervised data improve slot filling
Zhang, Y., Zhong, V., Chen, D., Angeli, G. & Manning, C. D · 2017
Cited alongside, same era.
DeepDive: Declarative knowledge base construction
Zhang, C. et al · 2017
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Lee, H. et al · 2018
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Assessment of convolutional neural networks for automated classification of chest radiographs
Dunnmon, J. A. et al · 2018
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Deep learning for chest radiograph diagnosis: A retrospective comparison of the CheXNeXt algorithm to practicing radiologists
Rajpurkar, P. et al · 2018
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Negbio: a high-performance tool for negation and uncertainty detection in radiology reports
Peng, Y. et al · 2018
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Deep learning algorithms for detection of critical findings in head ct scans: a retrospective study
Chilamkurthy, S. et al · 2018
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Attention-based deep multiple instance learning
Ilse, M., Tomczak, J. M. & Welling, M · 2018
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Chrononet: A deep recurrent neural network for abnormal eeg identification
Roy, S., Kiral-Kornek, I. & Harrer, S · 2018
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Deep convolutional neural network for the automated detection and diagnosis of seizure using EEG signals
Acharya, U. R., Oh, S. L., Hagiwara, Y., Tan, J. H. & Adeli, H · 2018
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Visual EEG reviewing times with SCORE EEG
Brogger, J. et al · 2018
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A guide to deep learning in healthcare
Esteva, A. et al · 2019
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Training complex models with multi-task weak supervision
Ratner, A. et al · 2019
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