I’m giving a seminar about deep learning in medical and biological image and video segmentation, organized by the Machine Learning Community (MLC) Dresden. I’ll be giving an introduction into the field and a hands-on tutorial. The event should be suitable for beginners as well as more advanced users in deep learning.
Deep learning has a great potential for biomedical image analysis. In particular, convolutional neural networks are an important class of machine learning techniques that can be trained to classify, detect, localize, and segment objects or abnormalities in different image modalities by learning to extract relevant image features. Recent studies have shown that deep learning-based systems can outperform human radiologists in accuracy on a variety of tasks, at only a fraction of the time and cost. In this seminar, I will provide a gentle introduction to convolutional neural networks and their application to biomedical image analysis tasks, highlighting both their strengths and limitations. In addition, we will have a hands-on session training a neural network to segment biomedical images, based on keras.