Christoph Baur

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

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

Office MI 03.13.042

Research on Deep Learning for Medical Applications

• Medical Image Segmentation
• Multiple Sclerosis Lesion Segmentation and Classification
• Unsupervised Anomaly Detection
• Unsupervised & Semi-Supervised Domain Adaptation
• Deep Directed Generative Models (Generative Adversarial Networks, Variational Auto-Encoders, VAE-GANs, Adversarial Auto-Encoders) for Medical Image Synthesis
• Catheter Detection in X-ray images
• Mitotic figure detection in histology images of breast cancer
• Crowdsourcing for Deep-Learning
• Skin Image Analysis

Student Projects

• [Bachelor IDP / Klin. Anwendungsprojekt] Developing a Webplatform for Visual Turing Tests [Open]
• [Bachelor IDP / Klin. Anwendungsprojekt] Exploring and Curating a new Skin Lesion Database with Longitudinal Data [Open]
• [BSc|MSc|IDP] Exploring Unsupervised Generative Modeling for Model Pretraining [Open]
• [BSc|MSc|IDP] Anatomical Priors in Unsupervised Anomaly Detection [Open]

Feel free to contact me if you are looking for a Bachelor Thesis, Master Thesis, IDP or Guided Research Project! Open projects will be announced here as well.

Collaboration with the Neuroradiology at Klinikum Rechts der Isar

Together with the Department for Neuroradiology located at Klinikum Rechts der Isar I am working on exciting Computational Imaging projects. More information is provided at http://www.neurokopfzentrum.med.tum.de/neuroradiologie/forschung_projekt_computational_imaging.html

Teaching

• Praktikum Machine Learning in Medical Imaging (2018WiSe)
• Lecture Grundlagen: Algorithmen und Datenstrukturen (2018SoSe)
• Praktikum Machine Learning in Medical Imaging (2018SoSe)
• Hauptseminar Deep Learning for Medical Applications (2017WiSe)
• Praktikum Machine Learning in Medical Imaging (2017WiSe)
• Hauptseminar Deep Learning for Medical Applications (2017SoSe)
• Lecture Grundlagen: Algorithmen und Datenstrukturen (2017SoSe)

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

2020
	C. Baur, R. Graf, B. Wiestler, S. Albarqouni, N. Navab SteGANomaly: Inhibiting CycleGAN? Steganography for Unsupervised Anomaly Detection in Brain MRI Accepted to the Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Lima, Peru, October 2020 (bib)
	C. Baur, B. Wiestler, S. Albarqouni, N. Navab Scale-Space Autoencoders for Unsupervised Anomaly Segmentation in Brain MRI Accepted to the Proceedings of the 23rd International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Lima, Peru, October 2020 A pre-print version is available online at arXiv. (bib)
2019
	M. Bui, C. Baur, N. Navab, S. Ilic, S. Albarqouni Adversarial Networks for Camera Pose Regression and Refinement Proceedings of the International Conference on Computer Vision Workshops, 27 October - 2 November, 2019, Seoul (bib)
	C. Baur, S. Albarqouni, N. Navab Fusing Unsupervised and Supervised Deep Learning for White Matter Lesion Segmentation Proceedings of the 2nd International Conference on Medical Imaging with Deep Learning (MIDL), will be held from July 8th to 10th, 2019 in London, United Kingdom. (bib)
	M. T. Shaban, C. Baur, N. Navab, S. Albarqouni StainGAN: Stain Style Transfer for Digital Histological Images Proceedings of IEEE International Symposium on Biomedical Imaging (ISBI), Venice, Italy A pre-print version is available online at arXiv. (bib)
2018
	C. Baur, B. Wiestler, S. Albarqouni, N. Navab Deep Autoencoding Models for Unsupervised Anomaly Segmentation in Brain MR Images Accepted to the Proceedings of the Brain Lesion Workshop at the 21th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Granada, Spain, September 2018 A pre-print version is available online at arXiv. (bib)
	C. Baur, S. Albarqouni, N. Navab Generating Highly Realistic Images of Skin Lesions with GANs Accepted to the Proceedings of the ISIC Skin Image Analysis Workshop at the 21th International Conference on Medical Image Computing and Computer Assisted Interventions (MICCAI), Granada, Spain, September 2018 A longer pre-print version is available at arXiv. (bib)
	C. Baur, S. Albarqouni, N. Navab MelanoGANs: High Resolution Skin Lesion Synthesis with GANs Submitted to the 1st International conference on Medical Imaging with Deep Learning (MIDL), Amsterdam, Netherlands, July 2018 A pre-print version is available online at arXiv. (bib)
	M. Bui, F. Bourier, C. Baur, F. Milletari, N. Navab, S. Demirci Robust Navigation Support in Lowest Dose Image Setting International Journal of Computer Assisted Radiology and Surgery, In Press (bib)
2017
	B. Wiestler, C. Baur, P. Eichinger, , T. Zhang, V. Biberacher, C. Zimmer, , J. Kirschke, S. Albarqouni Fully Automated Multiple Sclerosis lesion detection on multi-channel subtraction images through an integrated Computer Vision- Machine Learning pipeline Clinical Neuroradiolgy (2017) 27:S1-S118 (bib)
	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)