Our Deep Learning papers were featured in Biomedical Computation Review

Our Deep Learning papers were featured in the "Deep Learning and the Future of 
Biomedical Image Analysis" article published in the Biomedical Computation Review (BCR). It is worth mentioning that BCR magazine from Stanford University and supported by NIH. Here is the list of our papers:

S. Albarqouni, J. Fotouhi, N. Navab X-ray In-Depth Decomposition: Revealing The Latent Structures Accepted to Proceedings of the 20th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Quebec, Canada, September 2017 A pre-print version is available online at arXiv. (bib)

2016
	S. Albarqouni, S. Matl, M. Baust, N. Navab, S. Demirci Playsourcing: A Novel Concept for Knowledge Creation in Biomedical Research Proceedings of MICCAI Workshop on Large-scale Annotation of Biomedical data and Expert Label Synthesis, Athens, Greece, October 2016 The first two authors contribute equally to this paper. (bib)
	S. Albarqouni, C. Baur, F. Achilles, V. Belagiannis, S. Demirci, N. Navab AggNet: Deep Learning from Crowds for Mitosis Detection in Breast Cancer Histology Images IEEE Transactions on Medical Imaging (TMI), Special Issue on Deep Learning, vol. 35, no. 5, pp. 1313 - 1321, 2016. The first two authors contribute equally to this paper. (bib)

2016
	C. Baur, S. Albarqouni, S. Demirci, N. Navab, P. Fallavollita CathNets: Detection and Single-View Depth Prediction of Catheter Electrodes 7th International Conference on Medical Imaging and Augmented Reality (MIAR), 24-26 August, 2016, Bern, Switzerland. (Best Paper Award) (bib)