|MathWorks: Design, train, and tune deep learning models||13:30 – 14:20|
|Guest Speaker – Arnie Berlin, MathWorks:
Implementing Multiple AI Experiments for Head and Neck Tumor Segmentation
|14:20 – 14:50|
|Combined Q&A||14:50 – 15:00|
In the first part, we will take a deeper dive into designing, training, and tuning deep learning models. We will show how MATLAB’s deep learning apps can help you edit neural networks, and devise and run experiments. We will also show how to leverage cloud compute resources to speed up your training.
In the second part, Arnie Berlin gives a step-by-step explanation of the training workflow developed for research on head and neck tumor segmentation from 7 modality/channel MRI images. Semantic segmentation training on Multi-modal MRI datasets can be very time consuming, even on single-GPU computers. Scalability from local CPU-only and single-GPU computers for troubleshooting and refinement to cloud based, high performance, multi-GPU computers is desired to minimize the more extensive time required for leave-one-out training. Development of the workflow was a collaboration between the University of Freiburg Medical Research Center (UKLFR) and MathWorks.