One of the most successful modern deep-learning applications in medical imaging is image segmentation. From neurological pathology in MR volumes to fetal anatomy in ultrasound videos, from cellular structures in microscopic images to multiple organs in whole-body
CT scans, the list is ever expanding. This tutorial project will guide students to build and train a state-of-the-art convolutional neural network from scratch, then validate it on real patient data. The objective of this project is to obtain 1) basic understanding of machine learning approaches applied for medical image segmentation, 2) practical knowledge of essential components in building and testing deep learning algorithms, and
3) obtain hands-on experience in coding a deep segmentation network for real-world clinical applications.
prior experience in Python