Fetching the paper…
Reading the bibliography…
When reading images, radiologists generate text reports describing the findings therein.
Frome, A., Corrado, G.S., Shlens, J., Bengio, S., Dean, J., Ranzato, M., Mikolov, T.: Devise: A deep visual-semantic embedding model. Advances in neural information processing systems 26
2013
Earlier work this paper cites.
2017
Earlier work this paper cites.
Vaswani, A., Shazeer, N., Parmar, N., Uszkoreit, J., Jones, L., Gomez, A.N., Kaiser, L.u., Polosukhin, I.: Attention is all you need. In: Guyon, I., Luxburg, U.V., Bengio, S., Wallach, H., Fergus, R., Vishwanathan, S., Garnett, R. (eds.) Advances in Neural Information Processing Systems. vol. 30. Curran Associates, Inc. (2017), https://proceedings.neurips.cc/paper/2017/file/3f5ee243547dee91fbd053c1c4a845aa-Paper.pdf
2017
Earlier work this paper cites.
Wang, X., Peng, Y., Lu, L., Lu, Z., Bagheri, M., Summers, R.M.: 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. pp. 2097–2106 (2017)
2017
Earlier work this paper cites.
Qin, C., Yao, D., Shi, Y., Song, Z.: Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomedical engineering online 17
2018
Earlier work this paper cites.
Irvin, J., Rajpurkar, P., Ko, M., Yu, Y., Ciurea-Ilcus, S., Chute, C., Marklund, H., Haghgoo, B., Ball, R., Shpanskaya, K., et al.: Chexpert: A large chest radiograph dataset with uncertainty labels and expert comparison. In: Proceedings of the AAAI conference on artificial intelligence. vol. 33, pp. 590–597 (2019)
2019
Earlier work this paper cites.
Jaiswal, A.K., Tiwari, P., Kumar, S., Gupta, D., Khanna, A., Rodrigues, J.J.: Identifying pneumonia in chest x-rays: a deep learning approach. Measurement 145
2019
Earlier work this paper cites.
Johnson, A.E., Pollard, T.J., Berkowitz, S.J., Greenbaum, N.R., Lungren, M.P., Deng, C.y., Mark, R.G., Horng, S.: Mimic-cxr, a de-identified publicly available database of chest radiographs with free-text reports. Scientific data 6
2019
Earlier work this paper cites.
Kim, M., Yun, J., Cho, Y., Shin, K., Jang, R., Bae, H.j., Kim, N.: Deep learning in medical imaging. Neurospine 16
2019
Earlier work this paper cites.
Wang, W., Zheng, V.W., Yu, H., Miao, C.: A survey of zero-shot learning: Settings, methods, and applications. ACM Transactions on Intelligent Systems and Technology (TIST) 10
2019
Cited alongside, same era.
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. Medical image analysis 66
2020
Cited alongside, same era.
Chen, T., Kornblith, S., Norouzi, M., Hinton, G.: A simple framework for contrastive learning of visual representations. In: International conference on machine learning. pp. 1597–1607. PMLR (2020)
2020
Cited alongside, same era.
Miech, A., Alayrac, J.B., Smaira, L., Laptev, I., Sivic, J., Zisserman, A.: End-to-end learning of visual representations from uncurated instructional videos. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 9879–9889 (2020)
2020
Cited alongside, same era.
Huang, S.C., Shen, L., Lungren, M.P., Yeung, S.: Gloria: A multimodal global-local representation learning framework for label-efficient medical image recognition. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 3942–3951 (2021)
2021
Later among the works it cites.
Jia, C., Yang, Y., Xia, Y., Chen, Y.T., Parekh, Z., Pham, H., Le, Q., Sung, Y.H., Li, Z., Duerig, T.: Scaling up visual and vision-language representation learning with noisy text supervision. In: International Conference on Machine Learning. pp. 4904–4916. PMLR (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Seibold, C., Kleesiek, J., Schlemmer, H.P., Stiefelhagen, R.: Self-guided multiple instance learning for weakly supervised disease classification and localization in chest radiographs. In: Asian Conference on Computer Vision. pp. 617–634. Springer (2020)
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Wu, J.T., Wong, K.C., Gur, Y., Ansari, N., Karargyris, A., Sharma, A., Morris, M., Saboury, B., Ahmad, H., Boyko, O., et al.: Comparison of chest radiograph interpretations by artificial intelligence algorithm vs radiology residents. JAMA network open 3
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Chen, X., He, K.: Exploring simple siamese representation learning. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 15750–15758 (2021)
2021
Cited alongside, same era.
2021
Later among the works it cites.
2021
Later among the works it cites.
Radford, A., Kim, J.W., Hallacy, C., Ramesh, A., Goh, G., Agarwal, S., Sastry, G., Askell, A., Mishkin, P., Clark, J., et al.: Learning transferable visual models from natural language supervision. In: International Conference on Machine Learning. pp. 8748–8763. PMLR (2021)
2021
Later among the works it cites.
2021
Later among the works it cites.
National health service. https://www.england.nhs.uk , accessed: 2022-03-1
2022
Closest in time.