Fetching the paper…
Reading the bibliography…
While deep learning models become more widespread, their ability to handle unseen data and generalize for any scenario is yet to be challenged.
1907
Earlier work this paper cites.
Hirsch, F.R., Franklin, W.A., Gazdar, A.F., Bunn, P.A.: Early detection of lung cancer: clinical perspectives of recent advances in biology and radiology. Clinical Cancer Research 7
2001
Earlier work this paper cites.
Torralba, A., Efros, A.A., et al.: Unbiased look at dataset bias. In: Proceedings of the Conference on Computer Vision and Pattern Recognition. vol. 1, p. 7. Citeseer (2011)
2011
Earlier work this paper cites.
del Ciello, A., Franchi, P., Contegiacomo, A., Cicchetti, G., Bonomo, L., Larici, A.R.: Missed lung cancer: when, where, and why? Diagnostic and Interventional Radiology 23
2016
Earlier work this paper cites.
Ganin, Y., Ustinova, E., Ajakan, H., Germain, P., Larochelle, H., Laviolette, F., Marchand, M., Lempitsky, V.: Domain-adversarial training of neural networks. The Journal of Machine Learning Research 17
2016
Earlier work this paper cites.
Huang, G., Liu, Z., Van Der Maaten, L., Weinberger, K.Q.: Densely connected convolutional networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 4700–4708 (2017)
2017
Earlier work this paper cites.
Litjens, G., Kooi, T., Bejnordi, B.E., Setio, A.A.A., Ciompi, F., Ghafoorian, M., van der Laak, J.A., van Ginneken, B., Sánchez, C.I.: A survey on deep learning in medical image analysis. Medical Image Analysis 42
2017
Earlier work this paper cites.
2017
Cited alongside, same era.
2017
Cited alongside, same era.
AlBadawy, E.A., Saha, A., Mazurowski, M.A.: Deep learning for segmentation of brain tumors: Impact of cross-institutional training and testing. Medical physics 45
2018
Cited alongside, same era.
Chen, C., Dou, Q., Chen, H., Heng, P.A.: Semantic-aware generative adversarial nets for unsupervised domain adaptation in chest x-ray segmentation. In: Proceedings of the International Workshop on Machine Learning in Medical Imaging. Springer (2018)
2018
Cited alongside, same era.
Javanmardi, M., Tasdizen, T.: Domain adaptation for biomedical image segmentation using adversarial training. In: Proceedings of the 15th International Symposium on Biomedical Imaging. IEEE (2018)
2018
Later among the works it cites.
Madani, A., Moradi, M., Karargyris, A., Syeda-Mahmood, T.: Semi-supervised learning with generative adversarial networks for chest X-ray classification with ability of data domain adaptation. In: IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018). pp. 1038–1042. IEEE (apr 2018). https://doi.org/10.1109/ISBI.2018.8363749, https://ieeexplore.ieee.org/document/8363749/
2018
Later among the works it cites.
Mahmood, F., Chen, R., Durr, N.J.: Unsupervised reverse domain adaptation for synthetic medical images via adversarial training. IEEE Transactions on Medical Imaging 37
2018
Later among the works it cites.
Bustos, A., Pertusa, A., Salinas, J.M., de la Iglesia-Vayá, M.: Padchest: A large chest x-ray image dataset with multi-label annotated reports (2019)
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Dou, Q., Ouyang, C., Chen, C., Chen, H., Heng, P.A.: Unsupervised cross-modality domain adaptation of convnets for biomedical image segmentations with adversarial loss. In: Proceedings of the 27th International Joint Conference on Artificial Intelligence. pp. 691–697 (2018)
2018
Cited alongside, same era.
Gholami, A., Subramanian, S., Shenoy, V., Himthani, N., Yue, X., Zhao, S., Jin, P., Biros, G., Keutzer, K.: A novel domain adaptation framework for medical image segmentation. In: Proceedings of the International Medical Image Computing and Computer Assisted Intervention Brainlesion Workshop. pp. 289–298. Springer (2018)
2018
Cited alongside, same era.
Hoffman, J., Tzeng, E., Park, T., Zhu, J.Y., Isola, P., Saenko, K., Efros, A.A., Darrell, T.: Cycada: Cycle consistent adversarial domain adaptation. In: Proceedings of the International Conference on Machine Learning. p. 15 (2018)
2018
Cited alongside, same era.
2019
Closest in time.
2019
Closest in time.