Aim
Th goal of this competition is to develop a model to classify (and if present, segment) pneumothorax from a set of chest radiographic images.
Background
Imagine suddenly gasping for air, helplessly breathless for no apparent reason. Could it be a collapsed lung? In the future, your entry in this competition could predict the answer.
Pneumothorax can be caused by a blunt chest injury, damage from underlying lung disease, or most horrifying—it may occur for no obvious reason at all. On some occasions, a collapsed lung can be a life-threatening event.
Pneumothorax is usually diagnosed by a radiologist on a chest x-ray, and can sometimes be very difficult to confirm. An accurate AI algorithm to detect pneumothorax would be useful in a lot of clinical scenarios. AI could be used to triage chest radiographs for priority interpretation, or to provide a more confident diagnosis for non-radiologists.
Challenge
https://www.kaggle.com/c/siim-acr-pneumothorax-segmentation/
Ivo Matteo Baltruschat
Co-Founder / Research Scientist
Ivo Matteo Baltruschat studied Information and Electrical Engineering at the University of Applied Sciences, Hamburg between 2010 and 2014. In 2016, he finished his Master of Science in medical engineering science at the Universität zu Lübeck with his thesis on Deep Learning for Advanced Medical Applications. During DAISYlabs, he was a PhD student in the group of Tobias Knopp at the Institute for Biomedical Imaging, Hamburg University of Technology. His research covered the automatic analysis of medical x-ray images using machine learning methods, with a focus on applying deep learning to high-resolution medical x-ray images for computer-aided diagnosis.
Nils Gessert
Research Scientist
Maximilian Nielsen
Student
Rüdiger Schmitz
PhD Student
René Werner
Co-Founder / Head of scientific working group
René Werner co-founded DAISYlabs and led the scientific working group at the University Medical Center Hamburg-Eppendorf, coordinating deep learning research for biomedical image processing across UKE and TUHH.