PyTorch, which is a very popular modular deep learning framework for fast, flexible experimentation is an invaluable resource for such problems. It supports the seamless conversion of Numpy arrays into GPU tensors and vice versa. The dynamic computational graph allows us to change the network behavior on the fly unlike static graphs and due to Its highly modular nature helps in fast debugging.
Unlike other production-grade tools, Pytorch helps with lots of Research and Experimentation with novel architectures and is very useful to test ideas a bit more quickly and prototyping.
With Medical Imaging being the field most impacted by AI, our goal in this workshop is to give a good head start covering the heuristics of Medical Imaging, the concepts involved in it and how to code your way out.
This hack would be divided into two halves.
- First Half: Pytorch Introduction Duration: 1 hour The first half would be a gentle introduction to PyTorch framework. We will introduce the audience with the basics of PyTorch. This workshop will cover topics like:
- What is PyTorch? (Use cases and war stories)
- Tensor 101
- ndarray/Tensor library
- Numpy Bridge
- Fast CPU to GPU conversion of tensors
- The automatic differentiation engine or autograd
- The optimization package
- Scope of debugging
- Linear Code flow in Pytorch (One of the core philosophy of PyTorch)
- Saving and loading models
Second Half: Let’s dive in.
- Introduction to Radiology: What is radiology? What do the images look like? How is AI used here? How will AI help improve radiology practice?
- Introduction to Convolutional Neural Networks with Hands-on experience of coding Neural Networks and CNN using PyTorch.
- Introduction to classification networks.
- A 2D classification network Hands-On session for Liver Segmentation/Classification.
- Challenges faced in Medical Imaging and Deep Learning in general.
- End the session by talking about the bridge between literature and practical implementation.
