|10 September 2017, Quebec City, Canada|
CAMP Nominations and Awards at MICCAI 2017
Once again our students made the difference. Only 12 papers out of over 800 submissions to MICCAI 2017 where nominated for Young Investigator Awards (less than .02%). This list include 3 papers from CAMP.
|10 February 2017, |
TEDx talk on Medical Augmented Reality
Professor Navab gave a TEDx talk on Medical Augmented Reality.
|8 September 2017, |
PhD Defense by Loïc Peter
The task of semantic image segmentation, i.e. the simultaneous partitioning of an image into regions and the labeling of their content, is a fundamental objective in computer vision and medical image analysis. The need for segmentation algorithms can be encountered in diverse real-world scenarios. In some cases, the segmentation of a scene must be entirely automated, for example to act as a scene understanding tool to help the decision-making of an autonomous system. In other situations, a segmentation algorithm can assist a human user in annotating an image. In this case, the fully-automated aspect is no longer crucial and can be relaxed in favor of an improved flexibility via interactions with the user. In this thesis, we demonstrate how the statistical learning framework of decision forests can be exploited for these two scenarios and propose three contributions. First, we introduce an efficient and generic scale-adaptive forest training scheme for fully automated image understanding. In the context of large digital slides in histopathology, we propose an interactive and adaptive solution entangling segmentation with image exploration to facilitate the examination of these large images. Finally, we show how the segmentation task can be modeled as a Twenty Questions game towards hands-free human-computer interactions. Overall, we hope to demonstrate the effectiveness of decision forests for automated segmentation in diverse application domains. Moreover, we wish to emphasize the relevance of interactive settings and to introduce practical learning-based solutions in cases where standard interactive techniques are not directly applicable.
|2 August 2017, FMI 00.13.009A |
Talk on Sensitive Longitudinal Events.NeuroImaging and Morphometry
Structural magnetic resonance imaging data are frequently analyzed to reveal morphological changes of the human brain in dementia. Most contemporary imaging biomarkers are scalar values, such as the volume of a structure, and may miss the localized morphological variation of early presymptomatic disease progression. Neuroanatomical shape descriptors, however, can represent complex geometric information of individual anatomical regions and may demonstrate increased sensitivity in association studies. Yet, they remain largely unexplored. In this talk we will describe sensitive computational methods for the automated quantification of neuroanatomical morphometric estimates and their longitudinal changes. We will, furthermore, show that neuroanatomical shape features, such as lateral shape asymmetry, demonstrate increased sensitivity to detect early disease effects and can improve computer-aided diagnosis of dementia.
|8 August 2017, |
One paper accepted at IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI)
|5 August 2017, Vancouver, Canada|
2 papers accepted at IROS 2017
|25 July 2017, Venice, Italy|
5 papers accepted at ICCV 2017
We are happy to announce that 5 papers of our chair have been accepted at ICCV
|25 July 2017, Quebec City, Quebec, Canada|
11 papers accepted at MICCAI 2017
We are happy to announce that 11 papers of our chair have been accepted at MICCAI
|24 July 2017, Nantes, France|
2 papers accepted at ISMAR 2017
We are happy to announce that 2 papers of our chair have been accepted at ISMAR