for medical image segmentation applications Medical imaging is an
important step on diagnosis for surgical or chemical planning. Magnetic
resonance imaging (MRI) provides rich information for before and during
treatment to evaluate the treatment and lesion progress. In medical image
analysis domain, automated lesions segmentation is an important clinical
diagnostic task and very challenging. Inspired
by the promising results achieved by deep learning in many application
fields, an automated application based on adversarial training is a very
practical and interesting topic. Currently we have two datasets for
brain tumor segmentation and Liver tumor segmentation which will be selected and applied in this topic. [6,7]PlaceRecognizer If you like to travel, you most certainly have been at the
point where you stood somewhere in an unknown city and asked yourself: What
kind of building is this? What is it for? Who was the architect of this
building? Well, fear no more! Due to modern computer vision technology we might
be able to answer these questions for you right on your smartphone!
In this seminar topic we want to have a look at how to
create a robust deep learning model that is able to recognize buildings from a
given image. In order to do this we will need to gather training data (e.g.
from street view images) and think of a good network architecture and method
for training such a model. So if you are interested in data gathering, training
of deep neural networks and maybe also Android Development, this topic is
perfectly suited for you!