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Cited by 196

The DOI matches its Crossref record; 3 of the 3 references it cites were found in a registry or an index.

Radiology Objects in COntext (ROCO): A Multimodal Image Dataset

Pelka, Obioma, Koitka, Sven, Rückert, Johannes, Nensa, Felix, Friedrich, Christoph M.·2018·Lecture Notes in Computer Science
DOI 10.1007/978-3-030-01364-6_20
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Research works printed but not linked: 19

  1. Abadi, M., et al.: Tensorflow: a system for large-scale machine learning. In: Proceedings of the 12th USENIX Conference on Operating Systems Design and Implementation, Berkeley, CA, USA. USENIX Association (2016)
  2. Bodenreider, O.: The unified medical language system (UMLS): integrating biomedical terminology. Nucl. Acids Res. 32(Database–Issue), 267–270 (2004). https://doi.org/10.1093/nar/gkh061 · DOI · Paper page
  3. García Seco de Herrera, A., Kalpathy-Cramer, J., Demner Fushman, D., Antani, S., Müller, H.: Overview of the ImageCLEF 2013 medical tasks. In: Working Notes of CLEF 2013 - Conference and Labs of the Evaluation forum. CEUR-WS Proceedings Notes, vol. 1179, Valencia, Spain, 23–26 September 2013 (2013)
  4. García Seco de Herrera, A., Müller, H., Bromuri, S.: Overview of the ImageCLEF 2015 medical classification task. In: Working Notes of CLEF 2015 - Conference and Labs of the Evaluation Forum. CEUR-WS Proceedings Notes, vol. 1391, Toulouse, France, 8–11 September 2015 (2015)
  5. García Seco de Herrera, A., Schaer, R., Bromuri, S., Müller, H.: Overview of the ImageCLEF 2016 medical task. In: Working Notes of CLEF 2016 - Conference and Labs of the Evaluation forum, Évora. CEUR-WS Proceedings Notes, vol. 1609, Portugal, 5–8 September 2016 (2016)
  6. Koitka, S., Friedrich, C.M.: Optimized convolutional neural network ensembles for medical subfigure classification. In: Jones, G.J.F., et al. (eds.) CLEF 2017. LNCS, vol. 10456, pp. 57–68. Springer, Cham (2017). https://doi.org/10.1007/978-3-319-65813-1_5 · DOI
  7. Lin, T.-Y., et al.: Microsoft COCO: common objects in context. In: Fleet, D., Pajdla, T., Schiele, B., Tuytelaars, T. (eds.) ECCV 2014, Part V. LNCS, vol. 8693, pp. 740–755. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-10602-1_48 · DOI · Paper page
  8. Müller, H., Kalpathy-Cramer, J., Demner-Fushman, D., Antani, S.: Creating a classification of image types in the medical literature for visual categorization. In: Proceedings of SPIE 8319, Medical Imaging 2012: Advanced PACS-Based Imaging Informatics and Therapeutic Applications, 83190P, 23 February 2012, vol. 8425, p. 194 (2012). https://doi.org/10.1117/12.911186 · doi:10.1117/12.911186
  9. Müller, H., et al.: Overview of the CLEF 2010 medical image retrieval track. In: CLEF 2010 LABs and Workshops, Notebook Papers. CEUR-WS Proceedings Notes, vol. 1176, Padua, Italy, 22–23 September 2010 (2010). http://ceur-ws.org/Vol-1176/CLEF2010wn-ImageCLEF-MullerEt2010.pdf
  10. Pelka, O., Friedrich, C.M.: FHDO biomedical computer science group at medical classification task of ImageCLEF 2015. In: Working Notes of CLEF 2015 - Conference and Labs of the Evaluation forum, Toulouse, France, 8–11 September 2015 (2015). http://ceur-ws.org/Vol-1391/14-CR.pdf
  11. Pelka, O., Friedrich, C.M.: Keyword generation for biomedical image retrieval with recurrent neural networks. In: Working Notes of CLEF 2017 - Conference and Labs of the Evaluation Forum. CEUR-WS Proceedings Notes, vol. 1866, Dublin, Ireland, 11–14 September 2017 (2017)
  12. Pelka, O., Nensa, F., Friedrich, C.M.: Adopting semantic information of grayscale radiographs for image classification and retrieval. In: Proceedings of the 11th International Joint Conference on Biomedical Engineering Systems and Technologies (BIOSTEC 2018). BIOIMAGING, vol. 2, Funchal, Madeira, Portugal, 19–21 January 2018, pp. 179–187 (2018). https://doi.org/10.5220/0006732301790187 · DOI
  13. Rajpurkar, P., et al.: Chexnet: radiologist-level pneumonia detection on chest x-rays with deep learning. CoRR abs/1711.05225 (2017). https://arxiv.org/abs/1711.05225 · arXiv
  14. Roberts, R.J.: PubMed central: the GenBank of the published literature. Proc. Natl. Acad. Sci. U.S.A. 98(2), 381–382 (2001). https://doi.org/10.1073/pnas.98.2.381 · DOI · Paper page
  15. Russakovsky, O.: Imagenet large scale visual recognition challenge. Int. J. Comput. Vis. 115(3), 211–252 (2015). https://doi.org/10.1007/s11263-015-0816-y · DOI · Paper page
  16. Soldaini, L., Goharian, N.: QuickUMLS: a fast, unsupervised approach for medical concept extraction. In: MedIR Workshop, SIGIR (2016)
  17. Tommasi, T., Caputo, B., Welter, P., Güld, M.O., Deserno, T.M.: Overviewof the CLEF 2009 medical image annotation track. In: Multilingual Information Access Evaluation II. Multimedia Experiments - 10th Workshop of the Cross-Language Evaluation Forum, CLEF 2009, Corfu, Greece, 30 September–2 October 2009, pp. 85–93 (2009). https://doi.org/10.1007/978-3-642-15751-6_9 · doi:10.1007/978-3-642-15751-6_9
  18. Vinyals, O., Toshev, A., Bengio, S., Erhan, D.: Show and tell: lessons learned from the 2015 MSCOCO image captioning challenge. IEEE Trans. Pattern Anal. Mach. Intell. 39(4), 652–663 (2017). https://doi.org/10.1109/TPAMI.2016.2587640 · DOI · Paper page
  19. Xu, Y., Mo, T., Feng, Q., Zhong, P., Lai, M., Chang, E.I.: Deep learning of feature representation with multiple instance learning for medical image analysis. In: IEEE International Conference on Acoustics, Speech and Signal Processing, ICASSP 2014, Florence, Italy, 4–9 May 2014, pp. 1626–1630 (2014). https://doi.org/10.1109/ICASSP.2014.6853873 · DOI · Paper page

Cited by

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Metadata

Authors
Pelka, O., Koitka, S., Rückert, J., Nensa, F., & Friedrich, C. M.
Venue
Lecture Notes in Computer Science
Publisher
Springer International Publishing
Published
2018
Pages
180-189
ISSN
  • 0302-9743 (ISSN-L)
  • 1611-3349
DOI
10.1007/978-3-030-01364-6_20
OpenAlex
W2897980926
Cited by
196
References
3
FWCI
1.77
Type
conference paper
Language
en
Open access
closed
Licence
www.springer.com/tdm (tdm)

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CoChat. (2026). Citation check: Radiology Objects in COntext (ROCO): A Multimodal Image Dataset. https://app.cochat.ai/papers/10.1007/978-3-030-01364-6_20
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Pelka, O., Koitka, S., Rückert, J., Nensa, F., & Friedrich, C. M. (2018). Radiology Objects in COntext (ROCO): A Multimodal Image Dataset. Lecture Notes in Computer Science. https://doi.org/10.1007/978-3-030-01364-6_20

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