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AI-powered Medical Imaging has recently achieved enormous attention due to its ability to provide fast-paced healthcare diagnoses.
2015
Earlier work this paper cites.
2015
Earlier work this paper cites.
2016
Earlier work this paper cites.
K. He, X. Zhang, S. Ren, and J. Sun, “Deep residual learning for image recognition,” 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) , pp. 770–778, 2016
2016
Earlier work this paper cites.
C. Szegedy, V. Vanhoucke, S. Ioffe, J. Shlens, and Z. Wojna, “Rethinking the inception architecture for computer vision,” in CVPR , 2016, pp. 2818–2826
2016
Earlier work this paper cites.
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,” CVPR , Jul 2017. [Online]. Available: http://dx.doi.org/10.1109/CVPR.2017.369
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
T. Fukuda, M. Suzuki, G. Kurata, S. Thomas, J. Cui, and B. Ramabhadran, “Efficient knowledge distillation from an ensemble of teachers,” in INTERSPEECH , 2017
2017
Earlier work this paper cites.
2017
Earlier work this paper cites.
P. Rajpurkar, J. Irvin, K. Zhu, B. Yang, and others., “Chexnet: Radiologist-level pneumonia detection on chest x-rays with deep learning,” 2017
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
X. Wang, Y. Peng, L. Lu, Z. Lu, and R. M. Summers, “Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays,” in CVPR , 2018, pp. 9049–9058
2018
Cited alongside, same era.
T. Garipov, P. Izmailov, D. Podoprikhin, D. Vetrov, and A. G. Wilson, “Loss surfaces, mode connectivity, and fast ensembling of dnns,” in NeurIPS , 2018, pp. 8803–8812
2018
Cited alongside, same era.
2018
Cited alongside, same era.
2020
Later among the works it cites.
2020
Later among the works it cites.
Z. Yang, L. Shou, M. Gong, W. Lin, and D. Jiang, “Model compression with two-stage multi-teacher knowledge distillation for web question answering system,” WSDM , 2020
2020
Later among the works it cites.
A. Jaiswal, T. Li, C. Zander, Y. Han, J. F. Rousseau, Y. Peng, and Y. Ding, “Scalp-supervised contrastive learning for cardiopulmonary disease classification and localization in chest x-rays using patient metadata,” in 2021 IEEE International Conference on Data Mining (ICDM) . IEEE, 2021, pp. 1132–1137
2021
Later among the works it cites.
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Z. Li, C. Wang, M. Han, Y. Xue, W. Wei, L.-J. Li, and L. Fei-Fei, “Thoracic disease identification and localization with limited supervision,” in CVPR , 2018, pp. 8290–8299
2018
Cited alongside, same era.
P. Kumar, M. Grewal, and M. M. Srivastava, “Boosted cascaded convnets for multilabel classification of thoracic diseases in chest radiographs,” in International Conference Image Analysis and Recognition . Springer, 2018, pp. 546–552
2018
Cited alongside, same era.
J. Liu, G. Zhao, Y. Fei, M. Zhang, Y. Wang, and Y. Yu, “Align, attend and locate: Chest x-ray diagnosis via contrast induced attention network with limited supervision,” in ICCV , Oct 2019
2019
Cited alongside, same era.
M.-C. Wu, C.-T. Chiu, and K.-H. Wu, “Multi-teacher knowledge distillation for compressed video action recognition on deep neural networks,” ICASSP , pp. 2202–2206, 2019
2019
Cited alongside, same era.
2019
Cited alongside, same era.
S. Zhou, Y. Zhuang, and R. Meng, “Multi-category skin lesion diagnosis using dermoscopy images and deep cnn ensembles,” línea], ISIC Chellange , 2019
2019
Cited alongside, same era.
Y. Han, C. Chen, L. Tang, M. Lin, A. Jaiswal, S. Wang, A. Tewfik, G. Shih, Y. Ding, and Y. Peng, “Using radiomics as prior knowledge for thorax disease classification and localization in chest x-rays,” in AMIA Annual Symposium Proceedings , vol. 2021, 2021, p. 546
2021
Later among the works it cites.
A. Jaiswal, L. Tang, M. Ghosh, J. F. Rousseau, Y. Peng, and Y. Ding, “Radbert-cl: Factually-aware contrastive learning for radiology report classification,” in Machine Learning for Health . PMLR, 2021
2021
Later among the works it cites.
F. Yuan, L. Shou, J. Pei, W. Lin, M. Gong, Y. Fu, and D. Jiang, “Reinforced multi-teacher selection for knowledge distillation,” in AAAI , vol. 35, no. 16, 2021, pp. 14 284–14 291
2021
Later among the works it cites.
A. K. Jaiswal, H. Ma, T. Chen, Y. Ding, and Z. Wang, “Training your sparse neural network better with any mask,” in International Conference on Machine Learning . PMLR, 2022, pp. 9833–9844
2022
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 CVPR , 2017, pp. 2097–2106
2097
Closest in time.