Deep Learning with PyTorch for Medical Image Analysis

About Course

Learn how to apply deep learning techniques to medical imaging with this free course from Udemy. This comprehensive course teaches you how to use PyTorch to build and train deep learning models for various medical imaging tasks, including cancer segmentation, pneumonia classification, and cardiac detection.

You’ll cover important topics such as:

NumPy
Machine learning theory
Test/train/validation data splits
Model evaluation for regression and classification tasks
Tensors with PyTorch
Convolutional neural networks
Medical imaging
Interpretability of a network’s decision
PyTorch Lightning
Tumor segmentation
Three-dimensional data
And much more

This course offers unique insights into applying deep learning to complex medical problems. You’ll gain valuable skills and techniques that are highly sought after in the AI industry. Join the vibrant online community of students and teaching assistants to get support and share your learning journey.

Enroll now and start your journey in deep learning for medical image analysis – completely free!

Learn how to use NumPy
Learn classic machine learning theory principals
Foundations of Medical Imaging
Data Formats in Medical Imaging
Creating Artificial Neural Networks with PyTorch
Use PyTorch-Lightning for state of the art training
Visualize the decision of a CNN
2D & 3D data handling
Automatic Cancer Segmentation

Course Content

Introduction

A Message from the Professor
COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!

06:41
Installation and Environment Setup

17:49
Course Curriculum

01:18

Crash Course NumPy

Machine Learning Concepts Overview

PyTorch Basics

CNN – Convolutional Neural Networks

Introduction to CNNs

01:04
Understanding the MNIST data set

03:25
ANN with MNIST – Part One – Data

19:22
ANN with MNIST – Part Two – Creating the Network

10:34
ANN with MNIST – Part Three – Training

15:28
ANN with MNIST – Part Four – Evaluation

09:15
Image Filters and Kernels

11:35
Convolutional Layers

14:01
Pooling Layers

06:47
MNIST Data Revisited

02:11
MNIST with CNN – Code Along – Part One

18:21
MNIST with CNN – Code Along – Part Two

18:18
MNIST with CNN – Code Along – Part Three

08:57
Why do we need GPUs

13:07
Using GPUs for PyTorch

17:40

Medical Imaging – A short Introduction

Data Formats in Medical Imaging

Pneumonia-Classification

Cardiac-Detection

Atrium-Segmentation

Capstone-Project Lung Tumor Segmentation

3D Liver and Liver Tumor Segmentation

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About Course

What Will You Learn?

Course Content

Introduction

A Message from the Professor

COURSE OVERVIEW LECTURE – PLEASE DO NOT SKIP!

Installation and Environment Setup

Course Curriculum

Crash Course NumPy

Introduction to NumPy

NumPy Arrays

NumPy Arrays Part Two

NumPy Index Selection

NumPy Operations

NumPy Exercises

NumPy Exercise – Solutions

Machine Learning Concepts Overview

What is Machine Learning

Supervised Learning

Overfitting

Evaluating Performance – Classification Error Metrics

Evaluating Performance – Regression Error Metrics

PyTorch Basics

PyTorch Basics Introduction

Tensor Basics

Tensor Basics-Part Two

Tensor Operations

Tensor Operations-Part Two

PyTorch Basics – Exercise

PyTorch Basics – Exercise Solutions

CNN – Convolutional Neural Networks

Introduction to CNNs

Understanding the MNIST data set

ANN with MNIST – Part One – Data

ANN with MNIST – Part Two – Creating the Network

ANN with MNIST – Part Three – Training

ANN with MNIST – Part Four – Evaluation

Image Filters and Kernels

Convolutional Layers

Pooling Layers

MNIST Data Revisited

MNIST with CNN – Code Along – Part One

MNIST with CNN – Code Along – Part Two

MNIST with CNN – Code Along – Part Three

Why do we need GPUs

Using GPUs for PyTorch

Medical Imaging – A short Introduction

Introduction

X-RAY

CT

MRI

PET

Data Formats in Medical Imaging

Introduction

DICOM

DICOM-in-Python

NIfTI

NIfTI-in-Python

Preprocessing

Preprocessing-in-Python-Part-1

Preprocessing-in-Python-Part-2

Pneumonia-Classification

Introduction

Preprocessing

Train-01-Data-Loading

Train-02-Model-Creation

Train-03-Trainer

Train-04-Evaluation

Interpretability

Cardiac-Detection

01-Introduction

02-Preprocessing

03-Dataset-Part-1

04-Dataset-Part-2

Train-01-Data-Loading

Train-02-Model-Creation

Train-03-Evaluation

Atrium-Segmentation

01-Introduction

Preprocessing-01-Visualization