Fetching the paper…
Reading the bibliography…
The current study of cell architecture of inflammation in histopathology images commonly performed for diagnosis and research purposes excludes a lot of information available on the biopsy slide.
Jacobson, D.L., Gange, S.J., Rose, N.R., Graham, N.M.: Epidemiology and estimated population burden of selected autoimmune diseases in the united states. Clinical immunology and immunopathology 84
1997
Earlier work this paper cites.
He, K., Zhang, X., Ren, S., Sun, J.: Deep residual learningfor image recognition. ComputerScience (2015)
2015
Earlier work this paper cites.
Lerner, A., Jeremias, P., Matthias, T.: The world incidence and prevalence of autoimmune diseases is increasing. International Journal of Celiac Disease 3
2015
Earlier work this paper cites.
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical image computing and computer-assisted intervention. pp. 234–241. Springer (2015)
2015
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.
Hu, J., Shen, L., Sun, G.: Squeeze-and-excitation networks. In: Proceedings of the IEEE conference on computer vision and pattern recognition. pp. 7132–7141 (2018)
2018
Earlier work this paper cites.
2018
Earlier work this paper cites.
Zhou, Z., Rahman Siddiquee, M.M., Tajbakhsh, N., Liang, J.: Unet++: A nested u-net architecture for medical image segmentation. In: Deep learning in medical image analysis and multimodal learning for clinical decision support, pp. 3–11. Springer (2018)
2018
Earlier work this paper cites.
Dash, M., Londhe, N.D., Ghosh, S., Semwal, A., Sonawane, R.S.: Pslsnet: Automated psoriasis skin lesion segmentation using modified u-net-based fully convolutional network. Biomedical Signal Processing and Control 52
2019
Earlier work this paper cites.
Falcon, W., et al.: Pytorch lightning. GitHub. Note: https://github. com/PyTorchLightning/pytorch-lightning 3
2019
Cited alongside, same era.
Raghu, M., Zhang, C., Kleinberg, J., Bengio, S.: Transfusion: Understanding transfer learning for medical imaging. Advances in neural information processing systems 32
2019
Cited alongside, same era.
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International conference on machine learning. pp. 6105–6114. PMLR (2019)
2019
Cited alongside, same era.
Wightman, R.: Pytorch image models. https://github.com/rwightman/pytorch-image-models (2019). https://doi.org/10.5281/zenodo.4414861
2019
Cited alongside, same era.
Dinse, G.E., Parks, C.G., Weinberg, C.R., Co, C.A., Wilkerson, J., Zeldin, D.C., Chan, E.K., Miller, F.W.: Increasing prevalence of antinuclear antibodies in the united states. Arthritis & Rheumatology 72
Xie, C., Tan, M., Gong, B., Wang, J., Yuille, A.L., Le, Q.V.: Adversarial examples improve image recognition. In: Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition. pp. 819–828 (2020)
2020
Later among the works it cites.
Xie, Q., Luong, M.T., Hovy, E., Le, Q.V.: Self-training with noisy student improves imagenet classification. In: Proceedings of the IEEE/CVF conference on computer vision and pattern recognition. pp. 10687–10698 (2020)
2020
Later among the works it cites.
Brock, A., De, S., Smith, S.L., Simonyan, K.: High-performance large-scale image recognition without normalization. In: International Conference on Machine Learning. pp. 1059–1071. PMLR (2021)
2021
Later among the works it cites.
Liu, Z., Lin, Y., Cao, Y., Hu, H., Wei, Y., Zhang, Z., Lin, S., Guo, B.: Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE/CVF International Conference on Computer Vision. pp. 10012–10022 (2021)
2021
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
2020
Cited alongside, same era.
2020
Cited alongside, same era.
Ehrenfeld, M., Tincani, A., Andreoli, L., Cattalini, M., Greenbaum, A., Kanduc, D., Alijotas-Reig, J., Zinserling, V., Semenova, N., Amital, H., et al.: Covid-19 and autoimmunity. Autoimmunity reviews 19
2020
Cited alongside, same era.
Galeotti, C., Bayry, J.: Autoimmune and inflammatory diseases following covid-19. Nature Reviews Rheumatology 16
2020
Cited alongside, same era.
Stafford, I., Kellermann, M., Mossotto, E., Beattie, R., MacArthur, B., Ennis, S.: A systematic review of the applications of artificial intelligence and machine learning in autoimmune diseases. NPJ digital medicine 3
2020
Cited alongside, same era.
Later among the works it cites.
2021
Later among the works it cites.
Agarwal, V., Jhalani, H., Singh, P., Dixit, R.: Classification of melanoma using efficient nets with multiple ensembles and metadata. In: Proceedings of International Conference on Computational Intelligence. pp. 101–111. Springer (2022)
2022
Closest in time.
2022
Closest in time.
Tsakalidou, V.N., Mitsou, P., Papakostas, G.A.: Computer vision in autoimmune diseases diagnosis—current status and perspectives. In: Computational Vision and Bio-Inspired Computing, pp. 571–586. Springer (2022)
2022
Closest in time.
Van Buren, K., Li, Y., Zhong, F., Ding, Y., Puranik, A., Loomis, C.A., Razavian, N., Niewold, T.B.: Artificial intelligence and deep learning to map immune cell types in inflamed human tissue. Journal of Immunological Methods 505
2022
Closest in time.