Deep Learning for Medical Image Analysis
CMPUT 605

 

General Information

Lecture Hours:

Weekly meeting Thursday from 14h00 to 16h00

Lecture Room:

Zoom Meeting

Instructor:

Prof. Pierre Boulanger

Office:

Athabasca Hall 411

Phone:

780-709-1260

Email:

pierreb@cs.ualberta.ca

Course Text:

Weekly readings


Course Description

The past twenty years of clinical applications of multimodal medical imaging (CT, MRI, US, PET/CT/MR, etc.) has revolutionized how medicine is practiced today by improving disease diagnostic and treatment. In the last decade, Deep Neural Networks (DNN) usage in this field has opened new doors to process those images allowing to perform automatic segmentation, multimodal sensor fusion and registration, and computer-aided diagnosis. This course will review the various DNN architectures found in the literature and then explore how they can be used in practical clinical applications. Course work includes homework, programming assignments, reading, and discussion of research papers, presentations, and a final project.

 


Course Topics

The course will closely follow the book by Zhou et al. entitled "Deep Learning for Image Analysis" from Academic Press. It will be complemented by relevant articles and extra notes provided in class. The course will cover:

·     Basic Theory of Neural Networks and Deep Learning

·     Various DNN Architectures

·     Medical Image Segmentation Using DNN

·     Medical Image Registration Using DNN

·     Temporal Image Analysis Using DNN

·     Computer-Aided Diagnostics and Disease Quantification Using DNN

·     Software Tools for DNN

·     Optimization of DNN

Course Prerequisites

This course is intended for graduate students with a medical imaging background, radiology, and related fields. It is primarily intended for students who need more insight into how DNN can be applied to medical image processing. The course assumes a background in programming and good knowledge of image processing. In addition to the medical relevance of the topics being covered, the exposition to the theoretical concepts will be made more concrete by a set of well-selected programming exercises. Familiarity and experience with Python and with vector-matrix calculus is a prerequisite for this course.


Course Evaluation

30%

Homework

There will be regular homework assignments

55%

Project

There will be one team-based semester project, culminating in a final report and a haptics "open-house" where the project will be demonstrated. Progress and checkpoints before the last due date will count toward the final grade.

15%

Presentation

The presentation is a detailed lecture on a topic related to haptics, done individually. The lecturer also prepares a short discussion or group activity for after the talk.


Lecture Notes

Lecture Date

Topics

Slides

Extras

Jan. 14

Introduction

Introduction.pptx

NeuralNetworksinMedicalImagingApplications.pdf

Assignment 1

Assignment Due Jan. 29

Jan. 21

Basic Math of Deep Neural Networks

Understanding deep convolutional networks by Mallat

Deep Neural Network Mathematical Mysteries for High Dimensional Learning

 

Jan. 28 to Feb. 11

Basic Math of Deep Neural Networks

Deep-Leaning-Basics.pptx

Deep-Learning-Overview-MRI-2018.pdf

Chapter 1 and 2 in Class Book

Overview of Deep Learning in Medical Imaging

Assignment 2

Assignment Due Feb. 25

 

Feb. 18

U-Net and V-Net for Medical Image Segmentation

U-Net.pdf

u-net-teaser.mp4

V-Net.pdf

U2 Net

 

Project Proposal Due Feb. 25

Feb. 25

 

 

Deep Belief  Networks: Restrictive Boltzman Machines and Autoencoder

 

 

Tutorial on Deep Belief Networks: RBM and Autoencoders

TensorFlow Tutorial on Autoencoders

Assignment 3

Assignment Due Mar. 25

Mar. 4

A simple overview of RNN, LSTM and Attention Mechanism

RNN-LSTM-Tutorial

 

Mar. 11

Complex and Quaternion Neural Networks

Complex and Quaternion Neural Network Lecture

Quaternion Neural Networks

 

Mar. 18

Fourier Neural Networks

Fourier CNN Implementations

Fourier Neural Networks a Comparative Study

A Fourier Domain Training Framework

Tiled Fourier CNN

Accelerating Convolutional Neural Network

Mar. 25

Multi-level Wavelet Neural Networks

MWCNN Paper

Wavelet Neural Networks

 Assignment 4

Assignment Due Apr. 15

Apr. 1

Medical Image Registration Using Neural Networks

CNN-based Medical Image Registration

Deep learning in medical image registration: a survey      

 

Apr. 8

Spiked Neural Networks

 

Spiked Neural Networks

 

Apr. 15

Solving ODE Using Neural Network

 ODENet

 Project Report Due Apr. 22

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

 

Grading Policy