It’s a great pleasure to announce an invited talk from TU Munich by Roger David Soberanis Mukul in Beyond the Patterns.

Abstract: Organ segmentation is an essential pre-processing step in different computer-assisted tasks, and currently, deep convolutional neural networks lead the state-of-the-art.  However, the nature of the medical images can lead to errors in the segmentation process, generating false negative and false positive regions in the results. Recent works have shown that the uncertainty of deep convolutional neural networks (CNN) can provide helpful insights about potential errors in the network’s predictions. Inspired by these works and the recent graph convolutional networks, we propose using the CNN’s uncertainty to formulate the refinement process as a semi-supervised graph learning problem. To validate our method, we refine the predictions of a 2D U-Net, trained on the NIH pancreas dataset and the spleen dataset of the medical segmentation decathlon. Finally, we perform a sensitivity analysis on the parameters of our proposal. 

Short Bio: Roger Soberanis is a PhD student at the Chair for Computer Aided Medical Procedures and Augmented Reality, Technical University of Munich.  He studied a bachelor’s in computer engineering and a master’s in computer science at the Mathematics faculty of the Autonomous University of Yucatan, Mexico.  His work focus on deep convolutional and graph-convolutional networks for medical applications, with a particular interest in medical image segmentation.  

References
Paper https://www.melba-journal.org/article/18135-an-uncertainty-driven-gcn-refinement-strategy-for-organ-segmentation

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Music Reference: 
Damiano Baldoni - Thinking of You (Intro)
Damiano Baldoni - Poenia (Outro)