ChristophBaur

Chair for Computer Aided Medical Procedures & Augmented Reality
Lehrstuhl für Informatikanwendungen in der Medizin & Augmented Reality

Christoph Baur

Christoph Baur, M.Sc.
PhD Candidate
c.baur [@] tum.de

Address

Chair for Computer Aided Medical Procedures & Augmented Reality
Fakultät für Informatik
Technische Universität München
Boltzmannstr. 3
85748 Garching b. München

Office MI 03.13.056







Research on Deep Learning for Medical Applications

  • Medical Image Segmentation
  • Unsupervised & Semi-Supervised Domain Adaptation
  • Generative Adversarial Networks
  • Variational Autoencoders
  • Anomaly Detection

Student Projects

  • Unsupervised Anomaly Detection using Deep Generative Models
  • [Masters] Image-to-Image Translation with conditional GANs for MR Data
  • [IDP/GRP] A comparison of different loss functions for Image Segmentation in MR Data

Feel free to contact me or drop by at my office if you are looking for a Bachelor Thesis, Master Thesis, IDP or Guided Research Project in winter semester 2017! Open projects will be announced here as well.

Projects & Interests

  • MS Lesion Segmentation in multimodal & multidomain MRI data
  • Catheter Detection in X-ray images
  • Mitotic figure detection in histology images of breast cancer

Teaching

Scholarship & Awards

  • Best Paper Award at MIAR Conference 2016, Bern, Switzerland
  • 3rd rank in MICCAI-AMIDA13 challenge for Automatic Models for Mitosis Detection in Breast Cancer Histology Images
  • 3rd rank at the Intel Leibniz Challenge 2009
  • 1st rank at the Neoflash Coding Competition 2009

Publications

2017
C. Baur, S. Albarqouni, N. Navab
Semi-Supervised Learning for Fully Convolutional Networks
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.
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)
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)
2015
C. Baur, F. Milletari, V. Belagiannis, N. Navab, P. Fallavollita
Automatic 3D reconstruction of electrophysiology catheters from two-view monoplane C-arm image sequences
The 6th International Conference on Information Processing in Computer-Assisted Interventions (IPCAI) (bib)

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