Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction-Reference-Cited by-同舟云学术

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Studying Public Medical Images from the Open Access Literature and Social Networks for Model Training and Knowledge Extraction

Published:2019-12-24 Issue: Volume: Page:553-564
ISSN:0302-9743
Container-title:MultiMedia Modeling
language:
Short-container-title:

Author:

Müller Henning,Andrearczyk Vincent,del Toro Oscar Jimenez,Dhrangadhariya Anjani,Schaer Roger,Atzori Manfredo

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-030-37734-2_45

Reference40 articles.

1. Andrearczyk, V., Depeursinge, A., Müller, H.: Neural network training for cross-protocol radiomic feature standardization in computed tomography. J. Med. Imaging 6(2), 024008 (2019)

2. Lecture Notes in Computer Science;V Andrearczyk,2018

3. Apostolova, E., You, D., Xue, Z., Antani, S., Demner-Fushman, D., Thoma, G.R.: Image retrieval from scientific publications: text and image content processing to separate multi-panel figures. J. Am. Soc. Inf. Sci. 64, 893–908 (2013)

4. Bejnordi, B.E., et al.: Diagnostic assessment of deep learning algorithms for detection of lymph node metastases in women with breast cancer. J. Am. Med. Assoc. 318(22), 2199–2210 (2017)

5. Cheng, B., Stanley, R.J., De, S., Antani, S., Thoma, G.R.: Automatic detection of arrow annotation overlays in biomedical images. Int. J. Healthcare Inf. Syst. Inform. 6(4), 23–41 (2011)

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