I am heading the laboratory for Artificial Intelligence in Medical Imaging. I have previously completed post-doctoral training at the Medical Vision Group in the Computer Science and Artificial Intelligence Lab at MIT and the Lab for Computational Neuroimaging, Department of Neurology at Harvard medical school. I received my PhD and Diploma from the Computer Science Department at TU München.
Tel: 089 4400 56900
Email: christian at ai-med dot de
Networks: Research Gate | LinkedIn | ORCID
Publications: Google Scholar | DBLP | arXiv
Code: GitHub
Demo: QuickNAT
Old websites: MIT | TUM
Publications
Wachinger, Christian; Navab, Nassir Ultrasound specific similarity measures for three-dimensional mosaicing Journal Article In: SPIE Medical Imaging, San Diego, California, USA, pp. 69140F, 2008. @article{wachinger2008ultrasound, |
Wachinger, C; Navab, N Ultrasound specific similarity measures for three-dimensional mosaicing [6914-14] Inproceedings In: PROCEEDINGS-SPIE THE INTERNATIONAL SOCIETY FOR OPTICAL ENGINEERING, pp. 6914, International Society for Optical Engineering; 1999 2008. @inproceedings{wachinger2008ultrasoundb, |
Wachinger, Christian; Wein, Wolfgang; Navab, Nassir Three-dimensional ultrasound mosaicing Inproceedings In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 327–335, Springer Berlin Heidelberg 2007. @inproceedings{wachinger2007three, |
Keil, Andreas; Wachinger, Christian; Brinker, Gerhard; Thesen, Stefan; Navab, Nassir Patient position detection for SAR optimization in magnetic resonance imaging Inproceedings In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 49–57, Springer Berlin Heidelberg 2006. @inproceedings{keil2006patient, |
Wachinger, Christian; Somlo, Patric; Hudelot, Celine; Bloch, Isabelle Automatic Construction of Statistical Children Brain Atlas with MRI Journal Article In: 2005. @article{wachinger2005automatic, |
Wachinger, Christian; Keil, Andreas; Navab, Nassir Positioning on Magnetic Resonance Tomography Journal Article In: 2005. @article{wachinger2005positioning, |
Fingerle, Benjamin; Wachinger, Christian 5 The mathematics of (Auto-) Calibrating AR Systems Journal Article In: 2nd Joint Advanced Summer School 2004 Course 3: Ubiquitous Tracking for Augmented Reality, pp. 75, 2004. @article{fingerle20045, |
Wachinger, Christian; Pock, Michael; Rentrop, Peter Simulation of the inverted pendulum Journal Article In: 2004. @article{wachinger2004simulation, |
Narazani, Marla; Sarasua, Ignacio; Pölsterl, Sebastian; Lizarraga, Aldana; Yakushev, Igor; Wachinger, Christian Is a PET all you need? A multi-modal study for Alzheimer's disease using 3D CNNs Inproceedings In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), 0000. @inproceedings{Narazani2022, Alzheimer’s Disease (AD) is the most common form of dementia and often difficult to diagnose due to the multifactorial etiology of dementia. Recent works on neuroimaging-based computer-aided diagnosis with deep neural networks (DNNs) showed that fusing structural magnetic resonance images (sMRI) and fluorodeoxyglucose positron emission tomography (FDG-PET) leads to improved accuracy in a study population of healthy controls and subjects with AD. However, this result conflicts with the established clinical knowledge that FDG-PET better captures AD-specific pathologies than sMRI. Therefore, we propose a framework for the systematic evaluation of multi-modal DNNs and critically re-evaluate single- and multi-modal DNNs based on FDG-PET and sMRI for binary healthy vs. AD, and three-way healthy/mild cognitive impairment/AD classification. Our experiments demonstrate that a single-modality network using FDG-PET performs better than MRI (accuracy 0.91 vs 0.87) and does not show improvement when combined. This conforms with the established clinical knowledge on AD biomarkers, but raises questions about the true benefit of multi-modal DNNs. We argue that future work on multi-modal fusion should systematically assess the contribution of individual modalities following our proposed evaluation framework. Finally, we encourage the community to go beyond healthy vs. AD classification and focus on differential diagnosis of dementia, where fusing multi-modal image information conforms with a clinical need. |
Sarasua, Ignacio; Pölsterl, Sebastian; Wachinger, Christian CASHformer: Cognition Aware SHape Transformer for Longitudinal Analysis Inproceedings In: Medical Image Computing and Computer-Assisted Intervention (MICCAI), 0000. @inproceedings{Sarasua2022b, |
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