Fetching the paper…
Reading the bibliography…
Creating a large-scale dataset of abnormality annotation on medical images is a labor-intensive and costly task.
Castellino, R.A.: Computer aided detection (cad): an overview. Cancer Imaging 5
2005
Earlier work this paper cites.
Gromet, M.: Comparison of computer-aided detection to double reading of screening mammograms: review of 231,221 mammograms. American Journal of Roentgenology 190
2008
Earlier work this paper cites.
Deng, J., Dong, W., Socher, R., Li, L.J., Li, K., Fei-Fei, L.: Imagenet: A large-scale hierarchical image database. In: 2009 IEEE conference on computer vision and pattern recognition. pp. 248–255. Ieee (2009)
2009
Earlier work this paper cites.
Neuman, M.I., Lee, E.Y., Bixby, S., Diperna, S., Hellinger, J., Markowitz, R., Servaes, S., Monuteaux, M.C., Shah, S.S.: Variability in the interpretation of chest radiographs for the diagnosis of pneumonia in children. Journal of hospital medicine 7
2012
Earlier work this paper cites.
Zhou, B., Khosla, A., Lapedriza, A., Oliva, A., Torralba, A.: Learning deep features for discriminative localization. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 2921–2929 (2016)
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.
Lakhani, P., Sundaram, B.: Deep learning at chest radiography: automated classification of pulmonary tuberculosis by using convolutional neural networks. Radiology 284
2017
Earlier work this paper cites.
Lin, T.Y., Goyal, P., Girshick, R., He, K., Dollár, P.: Focal loss for dense object detection. In: Proceedings of the IEEE international conference on computer vision. pp. 2980–2988 (2017)
2017
Earlier work this paper cites.
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
Cited alongside, same era.
Li, Z., Wang, C., Han, M., Xue, Y., Wei, W., Li, L.J., Fei-Fei, L.: Thoracic disease identification and localization with limited supervision. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 8290–8299 (2018)
2018
Cited alongside, same era.
Peng, Y., Wang, X., Lu, L., Bagheri, M., Summers, R., Lu, Z.: Negbio: a high-performance tool for negation and uncertainty detection in radiology reports. AMIA Summits on Translational Science Proceedings 2018
2018
Cited alongside, same era.
Qin, C., Yao, D., Shi, Y., Song, Z.: Computer-aided detection in chest radiography based on artificial intelligence: a survey. Biomedical engineering online 17
Bekker, J., Davis, J.: Learning from positive and unlabeled data: A survey. Machine Learning 109
2020
Later among the works it cites.
Tam, L.K., Wang, X., Turkbey, E., Lu, K., Wen, Y., Xu, D.: Weakly supervised one-stage vision and language disease detection using large scale pneumonia and pneumothorax studies. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 45–55. Springer (2020), https://doi.org/10.1007/978-3-030-59719-1_5
2020
Later among the works it cites.
Bhalodia, R., Hatamizadeh, A., Tam, L., Xu, Z., Wang, X., Turkbey, E., Xu, D.: Improving pneumonia localization via cross-attention on medical images and reports. In: International Conference on Medical Image Computing and Computer-Assisted Intervention. pp. 571–581. Springer (2021), https://doi.org/10.1007/978-3-030-87196-3_53
2021
Later among the works it cites.
Garg, S., Wu, Y., Smola, A.J., Balakrishnan, S., Lipton, Z.: Mixture proportion estimation and pu learning: A modern approach. Advances in Neural Information Processing Systems 34
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2018
Cited alongside, same era.
Wang, X., Peng, Y., Lu, L., Lu, Z., Summers, R.M.: Tienet: Text-image embedding network for common thorax disease classification and reporting in chest x-rays. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 9049–9058 (2018)
2018
Cited alongside, same era.
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
Cited alongside, same era.
Johnson, A.E., Pollard, T.J., Greenbaum, N.R., Lungren, M.P., Deng, C.y., Peng, Y., Lu, Z., Mark, R.G., Berkowitz, S.J., Horng, S.: Mimic-cxr-jpg - chest radiographs with structured labels (version 2.0.0). PhysioNet (2019), https://doi.org/10.13026/8360-t248
2019
Cited alongside, same era.
Liu, J., Zhao, G., Fei, Y., Zhang, M., Wang, Y., Yu, Y.: Align, attend and locate: Chest x-ray diagnosis via contrast induced attention network with limited supervision. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10632–10641 (2019)
2019
Cited alongside, same era.
Wadden, D., Wennberg, U., Luan, Y., Hajishirzi, H.: Entity, relation, and event extraction with contextualized span representations. In: Proceedings of the 2019 Conference on Empirical Methods in Natural Language Processing and the 9th International Joint Conference on Natural Language Processing (EMNLP-IJCNLP). pp. 5784–5789 (Nov 2019). https://doi.org/10.18653/v1/D19-1585
2019
Cited alongside, same era.
2021
Later among the works it cites.
Jain, S., Agrawal, A., Saporta, A., Truong, S.Q., Bui, T., Chambon, P., Zhang, Y., Lungren, M.P., Ng, A.Y., Langlotz, C., et al.: Radgraph: Extracting clinical entities and relations from radiology reports (2021)
2021
Later among the works it cites.
Jain, S., Smit, A., Truong, S.Q., Nguyen, C.D., Huynh, M.T., Jain, M., Young, V.A., Ng, A.Y., Lungren, M.P., Rajpurkar, P.: Visualchexbert: addressing the discrepancy between radiology report labels and image labels. In: Proceedings of the Conference on Health, Inference, and Learning. pp. 105–115 (2021)
2021
Later among the works it cites.
Sait, S., Tombs, M.: Teaching medical students how to interpret chest x-rays: the design and development of an e-learning resource. Advances in Medical Education and Practice 12
2021
Later among the works it cites.
Wu, J.T., Agu, N.N., Lourentzou, I., Sharma, A., Paguio, J.A., Yao, J.S., Dee, E.C., Mitchell, W.G., Kashyap, S., Giovannini, A., Celi, L.A., Moradi, M.: Chest imagenome dataset for clinical reasoning. In: Thirty-fifth Conference on Neural Information Processing Systems Datasets and Benchmarks Track (Round 2) (2021)
2021
Later among the works it cites.
Han, Y., Chen, C., Tewfik, A., Glicksberg, B., Ding, Y., Peng, Y., Wang, Z.: Knowledge-augmented contrastive learning for abnormality classification and localization in chest x-rays with radiomics using a feedback loop. In: Proceedings of the IEEE/CVF Winter Conference on Applications of Computer Vision. pp. 2465–2474 (2022)
2022
Closest in time.