bottyBotz / MPHY0043

UCL Module: Artificial Intelligence for Surgery and Intervention

MPHY0043: Artificial Intelligence for Surgery and Intervention

UCL Module | MPBE | UCL Moodle Page

Term 1 (Autumn), Academic Year 2021-22

Contacts

Name	Email	Role
Yipeng Hu	[e-mail]	Module Lead
Zachary Baum	[e-mail]	Head TA
Iani Gayo	[e-mail]	Tutor
Nina Montana Brown	[e-mail]	Tutor

1. Programming and development

Python, numerical computing

All practical tutorials, group work and coursework projects in this module are based on Python, with a number of common libraries, including NumPy, SciPy and Matplotlib. For a refresher or relevant materials in medical image analysis, please have a look at the UCL Module MPHY0030 - Programming Foundations in Medical Image Analysis.

Machine learning, deep learning

This module uses two deep learning libraries, TensorFlow and PyTorch.

Guide and tutorial materials for the deep learning libraries are widely available, for example, from the UCL Module COMP0090 - Introduction to Deep Learning, with relevant materials designed for medical applications in the UCL Module MPHY0041 - Machine Learning in Medical Imaging.

MONAI is also used, with many dedicated deep learning functionalities designed for medical applications.

Development tools

Jupyter Notebook and Anaconda/Conda are frequently used in most tutorials and will be required for the assessed group work and coursework. Follow the Development Tools to set them up on your machine.

Although not required, it is encouraged to use Git with this repository. Tutorials for its basic uses are also widely available, e.g. Work with Git used in MPHY0030.

2. Tutorials

| tools | envs | learning type | applications | remarks |

Go to individual tutorial directory and read the readme.md file to get started.

Surgical Data Regression

Tutorial directory
Keywords: Classical machine learning, linear algebra, optimisation, NumPy, TensorFlow and PyTorch
Devlopement tools: Jupyter Notebook (via Anaconda)

Surgical Gesture and Skill

Tutorial directory
Keywords: supervised classification, PyTorch, 3D CNN, JIGSAWS
Devlopement tools: Anaconda with PyTorch

3D Medical Image Segmentation

Tutorial directory
Keywords: PyTorch, segmentation, MONAI U-Net, clinical imaging data
Devlopement tools: Jupyter Notebook (via Anaconda)

Image Registration

Tutorial directory
Keywords: PyTorch, Unsupervised registration, MONAI, MedNist dataset
Devlopement tools: Jupyter Notebook (via Anaconda)

Intraoperative Motion Classification

Tutorial directory
Keywords: TensorFlow, Keras, PointNet, simulated dataset
Devlopement tools: Anaconda with TensorFlow

Vision and Workflow

Tutorial directory
Keywords: TensorFlow Keras, Supervised classification, "off-the-shelf" networks, endoscopic video data
Devlopement tools: Anaconda with TensorFlow

3. Reading list

A collection of books and research papers, applying artificial intelligence to surgery and intervention, is provided in the Reading List.