Can Masses of Non-Experts Train Highly Accurate Image Classifiers?-Reference-Cited by-同舟云学术

Can Masses of Non-Experts Train Highly Accurate Image Classifiers?

Published:2014 Issue: Volume: Page:438-445
ISSN:0302-9743
Container-title:Medical Image Computing and Computer-Assisted Intervention – MICCAI 2014
language:
Short-container-title:

Author:

Maier-Hein Lena,Mersmann Sven,Kondermann Daniel,Bodenstedt Sebastian,Sanchez Alexandro,Stock Christian,Kenngott Hannes Gotz,Eisenmann Mathias,Speidel Stefanie

Publisher

Springer International Publishing

Link

http://link.springer.com/content/pdf/10.1007/978-3-319-10470-6_55

Reference11 articles.

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2. Lecture Notes in Computer Science;N. Ahmidi,2013

3. Katic, D., Wekerle, A.L., Görtler, J., Spengler, P., Bodenstedt, S., Röhl, S., Suwelack, S., Kenngott, H.G., Wagner, M., Müller-Stich, B.P., Dillmann, R., Speidel, S.: Context-aware augmented reality in laparoscopic surgery. Comp. Med. Imag. and Graph. 37(2), 174–182 (2013)

4. Allan, M., Ourselin, S., Thompson, S., Hawkes, D., Kelly, J., Stoyanov, D.: Toward detection and localization of instruments in minimally invasive surgery. IEEE T. Bio-med. Eng. 60(4), 1050–1058 (2013)

5. Ranard, B., Ha, Y., Meisel, Z., Asch, D., Hill, S., Becker, L., Seymour, A., Merchant, R.: Crowdsourcing - harnessing the masses to advance health and medicine, a systematic review. J. Gen. Intern. Med. 29(1), 187–203 (2014)

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