[39] N. Marini, S. Marchesin, S. Otálora, M. Wodzinski, A. Caputo, M. Van Rijthoven, W. Aswolinskiy, J.-M. Bokhorst, D. Podareanu, E. Petters, et al. Unleashing the potential of digital pathology data by training computer-aided diagnosis models without human annotations. NPJ digital medicine, 5(1):102, 2022.
[40] D. Morris, E. Müller-Budack, and R. Ewerth. Slideimages: a dataset for educational image classification. In Advances in Information Retrieval: 42nd European Conference on IR Research, ECIR 2020, Lisbon, Portugal, April 14–17, 2020, Proceedings, Part II 42, pages 289–296. Springer, 2020.
[41] M. Neumann, D. King, I. Beltagy, and W. Ammar. ScispaCy: Fast and Robust Models for Biomedical Natural Language Processing. In Proceedings of the 18th BioNLP Workshop and Shared Task, pages 319-327, Florence, Italy, Aug. 2019. Association for Computational Linguistics. doi: 10.18653/v1/W19-5034. URL https://www.aclweb.org/anthology/W19-5034.
[42] N. F. Noy, M. A. Musen, J. L. Mejino Jr, and C. Rosse. Pushing the envelope: challenges in a frame-based representation of human anatomy. Data & Knowledge Engineering, 48(3): 335-359, 2004.
[43] O. Pelka, S. Koitka, J. Rückert, F. Nensa, and C. M. Friedrich. Radiology objects in context (roco): a multimodal image dataset. In Intravascular Imaging and Computer Assisted Stenting and Large-Scale Annotation of Biomedical Data and Expert Label Synthesis: 7th Joint International Workshop, CVII-STENT 2018 and Third International Workshop, LABELS 2018, Held in conjunction with MICCAI 2018, Granada, Spain, September 16, 2018, Proceedings 3, pages 180-189. Springer, 2018.
[44] J. Pont-Tuset, J. Uijlings, S. Changpinyo, R. Soricut, and V. Ferrari. Connecting vision and language with localized narratives. In ECCV, 2020.
[45] A. Radford, J. Wu, R. Child, D. Luan, D. Amodei, I. Sutskever, et al. Language models are unsupervised multitask learners. OpenAI blog, 1(8):9, 2019.
[46] A. Radford, J. W. Kim, C. Hallacy, A. Ramesh, G. Goh, S. Agarwal, G. Sastry, A. Askell, P. Mishkin, J. Clark, et al. Learning transferable visual models from natural language supervision. In International conference on machine learning, pages 8748-8763. PMLR, 2021.
[47] A. Radford, J. W. Kim, T. Xu, G. Brockman, C. McLeavey, and I. Sutskever. Robust speech recognition via large-scale weak supervision. arXiv preprint arXiv:2212.04356, 2022.
[48] R. J. Roberts. Pubmed central: The genbank of the published literature, 2001.
[49] S. Rose, D. Engel, N. Cramer, and W. Cowley. Automatic keyword extraction from individual documents. Text mining: applications and theory, pages 1-20, 2010.
[50] T. Santos, A. Tariq, S. Das, K. Vayalpati, G. H. Smith, H. Trivedi, and I. Banerjee. Pathologybert-pre-trained vs. a new transformer language model for pathology domain. arXiv preprint arXiv:2205.06885, 2022.
[51] P. N. Schofield, J. P. Sundberg, B. A. Sundberg, C. McKerlie, and G. V. Gkoutsos. The mouse pathology ontology, mpath; structure and applications. Journal of biomedical semantics, 4(1): 1-8, 2013.
[52] C. Schuhmann, R. Beaumont, R. Vencu, C. Gordon, R. Wightman, M. Cherti, T. Coombes, A. Katta, C. Mullis, M. Wortzman, et al. Laion-5b: An open large-scale dataset for training next generation image-text models. arXiv preprint arXiv:2210.08402, 2022.
[53] J. Silva-Rodriguez, A. Colomer, M. A. Sales, R. Molina, and V. Naranjo. Going deeper through the gleason scoring scale: An automatic end-to-end system for histology prostate grading and cribriform pattern detection. Computer methods and programs in biomedicine, 195:105637, 2020.