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Supervised training of deep learning models requires large labeled datasets.
Y. Li, J. Yang, Y. Song, L. Cao, J. Luo, and L.-J. Li, “Learning from noisy labels with distillation,” in Proceedings of the IEEE International Conference on Computer Vision , 2017, pp. 1910–1918
1918
Earlier work this paper cites.
G. Patrini, A. Rozza, A. Krishna Menon, R. Nock, and L. Qu, “Making deep neural networks robust to label noise: A loss correction approach,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2017, pp. 1944–1952
1952
Earlier work this paper cites.
H. L. Kundel and G. Revesz, “Lesion conspicuity, structured noise, and film reader error,” American Journal of Roentgenology , vol. 126, no. 6, pp. 1233–1238, 1976
1976
Earlier work this paper cites.
J. R. Quinlan, “Induction of decision trees,” Machine learning , vol. 1, no. 1, pp. 81–106, 1986
1986
Earlier work this paper cites.
C. E. Brodley, M. A. Friedl et al. , “Identifying and eliminating mislabeled training instances,” in Proceedings of the National Conference on Artificial Intelligence , 1996, pp. 799–805
1996
Earlier work this paper cites.
D. R. Wilson and T. R. Martinez, “Instance pruning techniques,” in ICML , vol. 97, no. 1997, 1997, pp. 400–411
1997
Earlier work this paper cites.
Y. Freund, R. Schapire, and N. Abe, “A short introduction to boosting,” Journal-Japanese Society For Artificial Intelligence , vol. 14, no. 771-780, p. 1612, 1999
1999
Earlier work this paper cites.
L. G. Quekel, A. G. Kessels, R. Goei, and J. M. van Engelshoven, “Miss rate of lung cancer on the chest radiograph in clinical practice,” Chest , vol. 115, no. 3, pp. 720–724, 1999
1999
Earlier work this paper cites.
T. G. Dietterich, “An experimental comparison of three methods for constructing ensembles of decision trees: Bagging, boosting, and randomization,” Machine learning , vol. 40, no. 2, pp. 139–157, 2000
2000
Earlier work this paper cites.
——, “Reduction techniques for instance-based learning algorithms,” Machine learning , vol. 38, no. 3, pp. 257–286, 2000
2000
Earlier work this paper cites.
D. Gamberger, N. Lavrac, and S. Dzeroski, “Noise detection and elimination in data preprocessing: experiments in medical domains,” Applied Artificial Intelligence , vol. 14, no. 2, pp. 205–223, 2000
2000
Earlier work this paper cites.
W. C. Allsbrook Jr, K. A. Mangold, M. H. Johnson, R. B. Lane, C. G. Lane, and J. I. Epstein, “Interobserver reproducibility of gleason grading of prostatic carcinoma: general pathologist,” Human pathology , vol. 32, no. 1, pp. 81–88, 2001
2001
Earlier work this paper cites.
R. A. McDonald, D. J. Hand, and I. A. Eckley, “An empirical comparison of three boosting algorithms on real data sets with artificial class noise,” in International Workshop on Multiple Classifier Systems . Springer, 2003, pp. 35–44
2003
Earlier work this paper cites.
X. Zhu and X. Wu, “Class noise vs. attribute noise: A quantitative study,” Artificial intelligence review , vol. 22, no. 3, pp. 177–210, 2004
2004
Earlier work this paper cites.
C.-f. Lin et al. , “Training algorithms for fuzzy support vector machines with noisy data,” Pattern recognition letters , vol. 25, no. 14, pp. 1647–1656, 2004
2004
Earlier work this paper cites.
S. K. Warfield, K. H. Zou, and W. M. Wells, “Simultaneous truth and performance level estimation (staple): an algorithm for the validation of image segmentation,” IEEE transactions on medical imaging , vol. 23, no. 7, pp. 903–921, 2004
2004
Earlier work this paper cites.
A. Malossini, E. Blanzieri, and R. T. Ng, “Detecting potential labeling errors in microarrays by data perturbation,” Bioinformatics , vol. 22, no. 17, pp. 2114–2121, 2006
2006
Earlier work this paper cites.
H.-C. Kim and Z. Ghahramani, “Bayesian gaussian process classification with the em-ep algorithm,” IEEE Transactions on Pattern Analysis and Machine Intelligence , vol. 28, no. 12, pp. 1948–1959, 2006
2006
Earlier work this paper cites.
J.-w. Sun, F.-y. Zhao, C.-j. Wang, and S.-f. Chen, “Identifying and correcting mislabeled training instances,” in Future generation communication and networking (FGCN 2007) , vol. 1. IEEE, 2007, pp. 244–250
2007
Earlier work this paper cites.
R. Khardon and G. Wachman, “Noise tolerant variants of the perceptron algorithm,” Journal of Machine Learning Research , vol. 8, no. Feb, pp. 227–248, 2007
2007
Earlier work this paper cites.
A. Folleco, T. M. Khoshgoftaar, J. Van Hulse, and L. Bullard, “Identifying learners robust to low quality data,” in 2008 IEEE International Conference on Information Reuse and Integration . IEEE, 2008, pp. 190–195
2008
Earlier work this paper cites.
J. Deng, W. Dong, R. Socher, L.-J. Li, K. Li, and L. Fei-Fei, “Imagenet: A large-scale hierarchical image database,” in 2009 IEEE conference on computer vision and pattern recognition . Ieee, 2009, pp. 248–255
2009
Earlier work this paper cites.
N. Segata, E. Blanzieri, and P. Cunningham, “A scalable noise reduction technique for large case-based systems,” in International Conference on Case-Based Reasoning . Springer, 2009, pp. 328–342
2009
Earlier work this paper cites.
C. Zhang, C. Wu, E. Blanzieri, Y. Zhou, Y. Wang, W. Du, and Y. Liang, “Methods for labeling error detection in microarrays based on the effect of data perturbation on the regression model,” Bioinformatics , vol. 25, no. 20, pp. 2708–2714, 2009
2009
Earlier work this paper cites.
Y. Bengio, J. Louradour, R. Collobert, and J. Weston, “Curriculum learning,” in Proceedings of the 26th annual international conference on machine learning . ACM, 2009, pp. 41–48
2009
Earlier work this paper cites.
P. G. Ipeirotis, F. Provost, and J. Wang, “Quality management on amazon mechanical turk,” in Proceedings of the ACM SIGKDD workshop on human computation . ACM, 2010, pp. 64–67
2010
Earlier work this paper cites.
D. F. Nettleton, A. Orriols-Puig, and A. Fornells, “A study of the effect of different types of noise on the precision of supervised learning techniques,” Artificial intelligence review , vol. 33, no. 4, pp. 275–306, 2010
2010
Earlier work this paper cites.
J. Abellán and A. R. Masegosa, “Bagging decision trees on data sets with classification noise,” in International Symposium on Foundations of Information and Knowledge Systems . Springer, 2010, pp. 248–265
2010
Earlier work this paper cites.
P. M. Long and R. A. Servedio, “Random classification noise defeats all convex potential boosters,” Machine learning , vol. 78, no. 3, pp. 287–304, 2010
2010
Earlier work this paper cites.
B. Sluban, D. Gamberger, and N. Lavra, “Advances in class noise detection,” in Proceedings of the 2010 conference on ECAI 2010: 19th European Conference on Artificial Intelligence . IOS Press, 2010, pp. 1105–1106
2010
Earlier work this paper cites.
F. O. Kaster, B. H. Menze, M.-A. Weber, and F. A. Hamprecht, “Comparative validation of graphical models for learning tumor segmentations from noisy manual annotations,” in International MICCAI Workshop on Medical Computer Vision . Springer, 2010, pp. 74–85
2010
Earlier work this paper cites.
M. D. Reid and R. C. Williamson, “Composite binary losses,” Journal of Machine Learning Research , vol. 11, no. Sep, pp. 2387–2422, 2010
2010
Earlier work this paper cites.
V. C. Raykar, S. Yu, L. H. Zhao, G. H. Valadez, C. Florin, L. Bogoni, and L. Moy, “Learning from crowds,” Journal of Machine Learning Research , vol. 11, no. Apr, pp. 1297–1322, 2010
2010
Earlier work this paper cites.
V. Mnih and G. E. Hinton, “Learning to label aerial images from noisy data,” in Proceedings of the 29th International conference on machine learning (ICML-12) , 2012, pp. 567–574
2012
Earlier work this paper cites.
B. Frénay and M. Verleysen, “Classification in the presence of label noise: a survey,” IEEE transactions on neural networks and learning systems , vol. 25, no. 5, pp. 845–869, 2013
2013
Earlier work this paper cites.
N. Manwani and P. Sastry, “Noise tolerance under risk minimization,” IEEE transactions on cybernetics , vol. 43, no. 3, pp. 1146–1151, 2013
2013
Earlier work this paper cites.
N. Natarajan, I. S. Dhillon, P. K. Ravikumar, and A. Tewari, “Learning with noisy labels,” in Advances in neural information processing systems , 2013, pp. 1196–1204
2013
Earlier work this paper cites.
T. Donovan and D. Litchfield, “Looking for cancer: Expertise related differences in searching and decision making,” Applied Cognitive Psychology , vol. 27, no. 1, pp. 43–49, 2013
2013
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
2014
Earlier work this paper cites.
O. Ronneberger, P. Fischer, and T. Brox, “U-net: Convolutional networks for biomedical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention , 2015, pp. 234–241
2015
Earlier work this paper cites.
Y. LeCun, Y. Bengio, and G. Hinton, “Deep learning,” nature , vol. 521, no. 7553, p. 436, 2015
2015
Earlier work this paper cites.
D. Gurari, D. Theriault, M. Sameki, B. Isenberg, T. A. Pham, A. Purwada, P. Solski, M. Walker, C. Zhang, J. Y. Wong et al. , “How to collect segmentations for biomedical images? a benchmark evaluating the performance of experts, crowdsourced non-experts, and algorithms,” in 2015 IEEE winter conference on applications of computer vision . IEEE, 2015, pp. 1169–1176
2015
Earlier work this paper cites.
S. García, J. Luengo, and F. Herrera, Data preprocessing in data mining . Springer, 2015
2015
Earlier work this paper cites.
B. Van Rooyen, A. Menon, and R. C. Williamson, “Learning with symmetric label noise: The importance of being unhinged,” in Advances in Neural Information Processing Systems , 2015, pp. 10–18
2015
Earlier work this paper cites.
T. Liu and D. Tao, “Classification with noisy labels by importance reweighting,” IEEE Transactions on pattern analysis and machine intelligence , vol. 38, no. 3, pp. 447–461, 2015
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
P. D. Vo, A. Ginsca, H. Le Borgne, and A. Popescu, “Effective training of convolutional networks using noisy web images,” in 2015 13th International Workshop on Content-Based Multimedia Indexing (CBMI) . IEEE, 2015, pp. 1–6
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
T. Xiao, T. Xia, Y. Yang, C. Huang, and X. Wang, “Learning from massive noisy labeled data for image classification,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2015, pp. 2691–2699
2015
Earlier work this paper cites.
H. Izadinia, B. C. Russell, A. Farhadi, M. D. Hoffman, and A. Hertzmann, “Deep classifiers from image tags in the wild,” in Proceedings of the 2015 Workshop on Community-Organized Multimodal Mining: Opportunities for Novel Solutions . ACM, 2015, pp. 13–18
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
S. Song, K. Chaudhuri, and A. Sarwate, “Learning from data with heterogeneous noise using sgd,” in Artificial Intelligence and Statistics , 2015, pp. 894–902
2015
Earlier work this paper cites.
S. Ren, K. He, R. Girshick, and J. Sun, “Faster r-cnn: Towards real-time object detection with region proposal networks,” in Advances in neural information processing systems , 2015, pp. 91–99
2015
Earlier work this paper cites.
Y. Guo, L. Zhang, Y. Hu, X. He, and J. Gao, “Ms-celeb-1m: A dataset and benchmark for large-scale face recognition,” in European Conference on Computer Vision . Springer, 2016, pp. 87–102
2016
Earlier work this paper cites.
P. Bridge, A. Fielding, P. Rowntree, and A. Pullar, “Intraobserver variability: Should we worry?” Journal of medical imaging and radiation sciences , vol. 47, no. 3, pp. 217–220, 2016
2016
Earlier work this paper cites.
V. Gulshan, L. Peng, M. Coram, M. C. Stumpe, D. Wu, A. Narayanaswamy, S. Venugopalan, K. Widner, T. Madams, J. Cuadros et al. , “Development and validation of a deep learning algorithm for detection of diabetic retinopathy in retinal fundus photographs,” Jama , vol. 316, no. 22, pp. 2402–2410, 2016
2016
Earlier work this paper cites.
S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, and N. Navab, “Aggnet: deep learning from crowds for mitosis detection in breast cancer histology images,” IEEE transactions on medical imaging , vol. 35, no. 5, pp. 1313–1321, 2016
2016
Cited alongside, same era.
D. Ravì, C. Wong, F. Deligianni, M. Berthelot, J. Andreu-Perez, B. Lo, and G.-Z. Yang, “Deep learning for health informatics,” IEEE journal of biomedical and health informatics , vol. 21, no. 1, pp. 4–21, 2016
2016
Cited alongside, same era.
G. Patrini, F. Nielsen, R. Nock, and M. Carioni, “Loss factorization, weakly supervised learning and label noise robustness,” in International conference on machine learning , 2016, pp. 708–717
2016
Cited alongside, same era.
2016
Cited alongside, same era.
D. Hendrycks, M. Mazeika, D. Wilson, and K. Gimpel, “Using trusted data to train deep networks on labels corrupted by severe noise,” in Advances in Neural Information Processing Systems , 2018, pp. 10 456–10 465
2018
Later among the works it cites.
2018
Later among the works it cites.
Y. Wang, W. Liu, X. Ma, J. Bailey, H. Zha, L. Song, and S.-T. Xia, “Iterative learning with open-set noisy labels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 8688–8696
2018
Later among the works it cites.
2018
Later among the works it cites.
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C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 2818–2826
2016
Cited alongside, same era.
A. J. Ratner, C. M. De Sa, S. Wu, D. Selsam, and C. Ré, “Data programming: Creating large training sets, quickly,” in Advances in neural information processing systems , 2016, pp. 3567–3575
2016
Cited alongside, same era.
J. Goldberger and E. Ben-Reuven, “Training deep neural-networks using a noise adaptation layer,” 2016
2016
Cited alongside, same era.
A. J. Bekker and J. Goldberger, “Training deep neural-networks based on unreliable labels,” in 2016 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) . IEEE, 2016, pp. 2682–2686
2016
Cited alongside, same era.
I. Jindal, M. Nokleby, and X. Chen, “Learning deep networks from noisy labels with dropout regularization,” in 2016 IEEE 16th International Conference on Data Mining (ICDM) . IEEE, 2016, pp. 967–972
2016
Cited alongside, same era.
I. Misra, C. Lawrence Zitnick, M. Mitchell, and R. Girshick, “Seeing through the human reporting bias: Visual classifiers from noisy human-centric labels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2016, pp. 2930–2939
2016
Cited alongside, same era.
A. Shrivastava, A. Gupta, and R. Girshick, “Training region-based object detectors with online hard example mining,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2016, pp. 761–769
2016
Cited alongside, same era.
Ö. Çiçek, A. Abdulkadir, S. S. Lienkamp, T. Brox, and O. Ronneberger, “3d u-net: learning dense volumetric segmentation from sparse annotation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2016, pp. 424–432
2016
Cited alongside, same era.
D. Nie, Y. Gao, L. Wang, and D. Shen, “Asdnet: Attention based semi-supervised deep networks for medical image segmentation,” in International Conference on Medical Image Computing and Computer-Assisted Intervention . Springer, 2018, pp. 370–378
2018
Later among the works it cites.
L. Zhang, V. Gopalakrishnan, L. Lu, R. M. Summers, J. Moss, and J. Yao, “Self-learning to detect and segment cysts in lung ct images without manual annotation,” in 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018) . IEEE, 2018, pp. 1100–1103
2018
Later among the works it cites.
B. Han, Q. Yao, X. Yu, G. Niu, M. Xu, W. Hu, I. Tsang, and M. Sugiyama, “Co-teaching: Robust training of deep neural networks with extremely noisy labels,” in Advances in Neural Information Processing Systems , 2018, pp. 8527–8537
2018
Later among the works it cites.
Z. Yu, W. Liu, Y. Zou, C. Feng, S. Ramalingam, B. Vijaya Kumar, and J. Kautz, “Simultaneous edge alignment and learning,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 388–404
2018
Later among the works it cites.
Y. Ding, L. Wang, D. Fan, and B. Gong, “A semi-supervised two-stage approach to learning from noisy labels,” in 2018 IEEE Winter Conference on Applications of Computer Vision (WACV) . IEEE, 2018, pp. 1215–1224
2018
Later among the works it cites.
K. K. Thekumparampil, A. Khetan, Z. Lin, and S. Oh, “Robustness of conditional gans to noisy labels,” in Advances in Neural Information Processing Systems , 2018, pp. 10 271–10 282
2018
Later among the works it cites.
2018
Later among the works it cites.
S. Guo, W. Huang, H. Zhang, C. Zhuang, D. Dong, M. R. Scott, and D. Huang, “Curriculumnet: Weakly supervised learning from large-scale web images,” in Proceedings of the European Conference on Computer Vision (ECCV) , 2018, pp. 135–150
2018
Later among the works it cites.
D. Tanaka, D. Ikami, T. Yamasaki, and K. Aizawa, “Joint optimization framework for learning with noisy labels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2018, pp. 5552–5560
2018
Later among the works it cites.
2018
Later among the works it cites.
O. Commowick, A. Istace, M. Kain, B. Laurent, F. Leray, M. Simon, S. C. Pop, P. Girard, R. Ameli, J.-C. Ferré et al. , “Objective evaluation of multiple sclerosis lesion segmentation using a data management and processing infrastructure,” Scientific reports , vol. 8, no. 1, pp. 1–17, 2018
2018
Later among the works it cites.
E. Arvaniti, K. S. Fricker, M. Moret, N. J. Rupp, T. Hermanns, C. Fankhauser, N. Wey, P. J. Wild, J. H. Rueschoff, and M. Claassen, “Automated gleason grading of prostate cancer tissue microarrays via deep learning,” bioRxiv , p. 280024, 2018
2018
Later among the works it cites.
2018
Later among the works it cites.
E. J. Topol, “High-performance medicine: the convergence of human and artificial intelligence,” Nature medicine , vol. 25, no. 1, p. 44, 2019
2019
Closest in time.
E. Topol, Deep medicine: how artificial intelligence can make healthcare human again . Hachette UK, 2019
2019
Closest in time.
L. M. Prevedello, S. S. Halabi, G. Shih, C. C. Wu, M. D. Kohli, F. H. Chokshi, B. J. Erickson, J. Kalpathy-Cramer, K. P. Andriole, and A. E. Flanders, “Challenges related to artificial intelligence research in medical imaging and the importance of image analysis competitions,” Radiology: Artificial Intelligence , vol. 1, no. 1, p. e180031, 2019
2019
Closest in time.
2019
Closest in time.
Y. Xu, A. Hosny, R. Zeleznik, C. Parmar, T. Coroller, I. Franco, R. H. Mak, and H. J. Aerts, “Deep learning predicts lung cancer treatment response from serial medical imaging,” Clinical Cancer Research , vol. 25, no. 11, pp. 3266–3275, 2019
2019
Closest in time.
2019
Closest in time.
C. P. Langlotz, B. Allen, B. J. Erickson, J. Kalpathy-Cramer, K. Bigelow, T. S. Cook, A. E. Flanders, M. P. Lungren, D. S. Mendelson, J. D. Rudie et al. , “A roadmap for foundational research on artificial intelligence in medical imaging: From the 2018 nih/rsna/acr/the academy workshop,” Radiology , vol. 291, no. 3, pp. 781–791, 2019
2019
Closest in time.
V. Cheplygina, M. de Bruijne, and J. P. Pluim, “Not-so-supervised: a survey of semi-supervised, multi-instance, and transfer learning in medical image analysis,” Medical image analysis , vol. 54, pp. 280–296, 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
J. Speth and E. M. Hand, “Automated label noise identification for facial attribute recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 25–28
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
A. Rusiecki, “Trimmed robust loss function for training deep neural networks with label noise,” in International Conference on Artificial Intelligence and Soft Computing . Springer, 2019, pp. 215–222
2019
Closest in time.
H. Le, D. Samaras, T. Kurc, R. Gupta, K. Shroyer, and J. Saltz, “Pancreatic cancer detection in whole slide images using noisy label annotations,” in Medical Image Computing and Computer Assisted Intervention – MICCAI 2019 . Springer International Publishing, 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
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Y. Shen and S. Sanghavi, “Learning with bad training data via iterative trimmed loss minimization,” in International Conference on Machine Learning , 2019, pp. 5739–5748
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
W. Zhang, Y. Wang, and Y. Qiao, “Metacleaner: Learning to hallucinate clean representations for noisy-labeled visual recognition,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7373–7382
2019
Closest in time.
Y. Zhong, W. Deng, M. Wang, J. Hu, J. Peng, X. Tao, and Y. Huang, “Unequal-training for deep face recognition with long-tailed noisy data,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 7812–7821
2019
Closest in time.
J. A. Fries, P. Varma, V. S. Chen, K. Xiao, H. Tejeda, P. Saha, J. Dunnmon, H. Chubb, S. Maskatia, M. Fiterau et al. , “Weakly supervised classification of aortic valve malformations using unlabeled cardiac mri sequences,” BioRxiv , p. 339630, 2019
2019
Closest in time.
D. Acuna, A. Kar, and S. Fidler, “Devil is in the edges: Learning semantic boundaries from noisy annotations,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 11 075–11 083
2019
Closest in time.
2019
Closest in time.
J. M. Köhler, M. Autenrieth, and W. H. Beluch, “Uncertainty based detection and relabeling of noisy image labels,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition Workshops , 2019, pp. 33–37
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
F. Chiaroni, M. Rahal, N. Hueber, and F. Dufaux, “Hallucinating a cleanly labeled augmented dataset from a noisy labeled dataset using gans,” 2019
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
2019
Closest in time.
X. Yu, B. Han, J. Yao, G. Niu, I. Tsang, and M. Sugiyama, “How does disagreement help generalization against label corruption?” in International Conference on Machine Learning , 2019, pp. 7164–7173
2019
Closest in time.
J. Li, Y. Wong, Q. Zhao, and M. S. Kankanhalli, “Learning to learn from noisy labeled data,” in Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition , 2019, pp. 5051–5059
2019
Closest in time.
S. R. Hashemi, S. S. M. Salehi, D. Erdogmus, S. P. Prabhu, S. K. Warfield, and A. Gholipour, “Asymmetric loss functions and deep densely-connected networks for highly-imbalanced medical image segmentation: Application to multiple sclerosis lesion detection,” IEEE Access , vol. 7, pp. 1721–1735, 2019
2019
Closest in time.
D. Karimi, G. Nir, L. Fazli, P. Black, L. Goldenberg, and S. Salcudean, “Deep learning-based gleason grading of prostate cancer from histopathology images-role of multiscale decision aggregation and data augmentation.” IEEE journal of biomedical and health informatics , 2019
2019
Closest in time.
X. Wang, Y. Peng, L. Lu, Z. Lu, M. Bagheri, and R. M. Summers, “Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases,” in Proceedings of the IEEE conference on computer vision and pattern recognition , 2017, pp. 2097–2106
2097
Closest in time.