Mô tả

Did you ever want to apply Deep Neural Networks to more than MNIST, CIFAR10 or cats vs dogs?

Do you want to learn about state of the art Machine Learning frameworks while segmenting cancer in CT-images?

Then this is the right course for you!

Welcome to one of the most comprehensive courses on  Deep Learning in medical imaging!

This course focuses on the application of state of the art Deep Learning architectures to various medical imaging challenges.

You will tackle several different tasks, including cancer segmentation, pneumonia classification, cardiac detection, Interpretability and many more.

The following topics are covered:

  • NumPy

  • Machine Learning Theory

  • Test/Train/Validation Data Splits

  • Model Evaluation - Regression and Classification Tasks

  • Tensors with PyTorch

  • Convolutional Neural Networks

  • Medical Imaging

  • Interpretability of a network's decision - Why does the network do what it does?

  • A state of the art high level pytorch library: pytorch-lightning

  • Tumor Segmentation

  • Three-dimensional data

  • and many more

Why choose this specific Deep Learning with PyTorch for Medical Image Analysis course ?

  • This course provides unique knowledge on the application of deep learning to highly complex and  non-standard (medical) problems (in 2D and 3D)

  • All lessons include clearly summarized theory and code-along examples, so that you can understand and follow every step.

  • Powerful online community with our QA Forums with thousands of students and dedicated Teaching Assistants, as well as student interaction on our Discord Server.

  • You will learn skills and techniques that the vast majority of AI engineers do not have!

--------------

Jose, Marcel, Sergios & Tobias






Bạn sẽ học được gì

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

Yêu cầu

  • Understanding of Python Basic Topics (data types,loops,functions) also Python OOP recommended
  • Ideally PyTorch, but not necessarily required

Nội dung khoá học

13 sections

Introduction

5 lectures
COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
06:41
Link to Download the Course Files
00:11
Installation and Environment Setup
17:49
Installation without yml file
00:16
Course Curriculum
01:18

Crash Course: NumPy

7 lectures
Introduction to NumPy
02:14
NumPy Arrays
10:45
NumPy Arrays Part Two
08:10
NumPy Index Selection
12:16
NumPy Operations
06:46
NumPy Exercises
01:18
NumPy Exercise - Solutions
07:05

Machine Learning Concepts Overview

6 lectures
What is Machine Learning
03:40
Supervised Learning
08:21
Overfitting
07:59
Evaluating Performance - Classification Error Metrics
16:37
Evaluating Performance - Regression Error Metrics
05:36
Recap: Machine Learning Concepts
3 questions

PyTorch Basics

7 lectures
PyTorch Basics Introduction
03:20
Tensor Basics
08:10
Tensor Basics-Part Two
15:12
Tensor Operations
13:29
Tensor Operations-Part Two
06:27
PyTorch Basics - Exercise
02:33
PyTorch Basics - Exercise Solutions
05:21

CNN - Convolutional Neural Networks

16 lectures
Introduction to CNNs
01:03
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
IMPORTANT: Library Difference between video and notebook
00:18
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

6 lectures
Introduction
05:17
Overview: X-RAY
03:10
Overview: CT
04:04
Overview: MRI
03:18
Overview: PET
03:04
Recap: Medical Imaging
4 questions

Data Formats in Medical Imaging

11 lectures
Introduction
01:55
DICOM
05:18
DICOM-in-Python
15:46
Recap: DICOM
3 questions
NIfTI
02:37
NIfTI-in-Python
09:19
Recap:NIfTI
3 questions
Preprocessing
14:44
Preprocessing-in-Python-Part-1
13:14
Preprocessing-in-Python-Part-2
12:10
Recap: Preprocessing
4 questions

Pneumonia-Classification

8 lectures
Introduction
12:44
Preprocessing
15:21
Train-01-Data-Loading
13:39
Train-02-Model-Creation
12:23
Train-03-Trainer
04:15
Train-04-Evaluation
09:03
Interpretability
17:40
Recap: Pneumonia-Classification
8 questions

Cardiac-Detection

7 lectures
01-Introduction
05:30
02-Preprocessing
13:16
03-Dataset-Part-1
12:03
04-Dataset-Part-2
05:02
Train-01-Data-Loading
04:36
Train-02-Model-Creation
15:20
Train-03-Evaluation
06:52

Atrium-Segmentation

9 lectures
01-Introduction
08:42
Preprocessing-01-Visualization
08:56
Preprocessing-02-Processing
08:00
Dataset-01-Dataset-Creation
08:47
Dataset-02-Dataset-Validation
04:00
UNet
14:18
Train-01-Data-Loading-and-Loss
05:54
Train-02-Model-Creation
09:31
Train-03-Evaluation
09:26

Capstone-Project: Lung Tumor Segmentation

5 lectures
Introduction
04:54
Overview
01:09
Oversampling
05:50
Hint - RuntimeError: expected scalar type Double but found Float
00:09
Discussion
05:17

3D Liver and Liver Tumor Segmentation

6 lectures
Introduction
11:19
Data-Visualization
05:06
Model
04:25
Train-01-TorchIO-Dataset
11:33
Train-02-Model-Creation
06:14
Train-03-Evaluation
08:29

BONUS SECTION: THANK YOU!

1 lectures
BONUS LECTURE
00:10

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