[21] F. Gilardi, M. Alizadeh, and M. Kubli. Chatgpt outperforms crowd-workers for text-annotation tasks. arXiv preprint arXiv:2303.15056, 2023.
[22] X. He, Y. Zhang, L. Mou, E. Xing, and P. Xie. Pathvqa: 30000+ questions for medical visual question answering. arXiv preprint arXiv:2003.10286, 2020.
[23] S.-C. Huang, L. Shen, M. P. Lungren, and S. Yeung. 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, pages 3942-3951, 2021.
[24] Z. Huang, F. Bianchi, M. Yuksekgonul, T. Montine, and J. Zou. Leveraging medical twitter to build a visual-language foundation model for pathology ai. bioRxiv, pages 2023-03, 2023.
[25] E. Hussain, L. B. Mahanta, H. Borah, and C. R. Das. Liquid based-cytology pap smear dataset for automated multi-class diagnosis of pre-cancerous and cervical cancer lesions. Data in brief, 30:105589, 2020.
[26] W. O. Ikezogwo, M. S. Seyfioglu, and L. Shapiro. Multi-modal masked autoencoders learn compositional histopathological representations. arXiv preprint arXiv:2209.01534, 2022.
[27] K. Jobin, A. Mondal, and C. Jawahar. Docfigure: A dataset for scientific document figure classification. In 2019 International Conference on Document Analysis and Recognition Workshops (ICDARW), volume 1, pages 74-79. IEEE, 2019.
[28] A. E. Johnson, T. J. Pollard, N. R. Greenbaum, M. P. Lungren, C.-y. Deng, Y. Peng, Z. Lu, R. G. Mark, S. J. Berkowitz, and S. Horng. Mimic-cxr-jpg, a large publicly available database of labeled chest radiographs. arXiv preprint arXiv:1901.07042, 2019.
[29] S. Jupp, J. Malone, T. Burdett, J.-K. Heriche, E. Williams, J. Ellenberg, H. Parkinson, and G. Rustici. The cellular microscopy phenotype ontology. Journal of biomedical semantics, 7: 1-8, 2016.
[30] Z. Karishma. Scientific document figure extraction, clustering and classification. 2021.
[31] J. N. Kather, N. Halama, and A. Marx. 100,000 histological images of human colorectal cancer and healthy tissue. Zenodo10, 5281, 2018.
[32] A. Kembhavi, M. Salvato, E. Kolve, M. Seo, H. Hajishirzi, and A. Farhadi. A diagram is worth a dozen images. In Computer Vision-ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part IV 14, pages 235-251. Springer, 2016.
[33] T. Kojima, S. S. Gu, M. Reid, Y. Matsuo, and Y. Iwasawa. Large language models are zero-shot reasoners. arXiv preprint arXiv:2205.11916, 2022.
[34] K. Kriegsmann, F. Lobers, C. Zgorzelski, J. Kriegsmann, C. Janßen, R. R. Meliß, T. Muley, U. Sack, G. Steinbuss, and M. Kriegsmann. Deep learning for the detection of anatomical tissue structures and neoplasms of the skin on scanned histopathological tissue sections. Frontiers in Oncology, 12, 2022.
[35] J. Liu, Q. Wang, H. Fan, S. Wang, W. Li, Y. Tang, D. Wang, M. Zhou, and L. Chen. Automatic label correction for the accurate edge detection of overlapping cervical cells. arXiv preprint arXiv:2010.01919, 2020.
[36] S. Liu, C. Zhu, F. Xu, X. Jia, Z. Shi, and M. Jin. Bci: Breast cancer immunohistochemical image generation through pyramid pix2pix. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 1815-1824, 2022.
[37] Z. Liu, P. Luo, X. Wang, and X. Tang. Deep learning face attributes in the wild. In Proceedings of International Conference on Computer Vision (ICCV), December 2015.
[38] Z. Liu, H. Mao, C.-Y. Wu, C. Feichtenhofer, T. Darrell, and S. Xie. A convnet for the 2020s. In Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition, pages 11976-11986, 2022.