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Jose Portilla, Marcel Früh, Sergios Gatidis, and Tobias Hepp

Did you ever want to apply Deep Neural Networks to more than

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.

Read more

Did you ever want to apply Deep Neural Networks to more than

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

Enroll now

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What's inside

Learning objectives

  • 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

Syllabus

Introduction
COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
Link to Download the Course Files
Installation and Environment Setup
Read more
Installation without yml file
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
Recap: Machine Learning Concepts
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
IMPORTANT: Library Difference between video and notebook
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
Overview: X-RAY
Overview: CT
Overview: MRI
Overview: PET
Recap: Medical Imaging
ToDO
DICOM
DICOM-in-Python
Recap: DICOM
NIfTI
NIfTI-in-Python
Recap:NIfTI
Preprocessing
Preprocessing-in-Python-Part-1
Preprocessing-in-Python-Part-2
Recap: Preprocessing
Build a deep learning model which is able to classify whether an x-ray image contains a pneumonia or not
Train-01-Data-Loading
Train-02-Model-Creation
Train-03-Trainer
Train-04-Evaluation
Interpretability
Recap: Pneumonia-Classification
Cardiac-Detection
01-Introduction
02-Preprocessing
03-Dataset-Part-1
04-Dataset-Part-2
Train-03-Evaluation
Atrium-Segmentation
Preprocessing-01-Visualization
Preprocessing-02-Processing
Dataset-01-Dataset-Creation
Dataset-02-Dataset-Validation
UNet
Train-01-Data-Loading-and-Loss
Capstone-Project: Lung Tumor Segmentation
Overview
Oversampling
Hint - RuntimeError: expected scalar type Double but found Float
Discussion
3D Liver and Liver Tumor Segmentation

Good to know

Know what's good
, what to watch for
, and possible dealbreakers
Taught by experts in the field of medical image analysis, such as Jose Portilla and Marcel Früh
Develops advanced AI engineering knowledge and skills that are in high demand across industries
Strong focus on practical implementation with hands-on coding exercises
Provides comprehensive coverage of Deep Learning architectures and techniques
Course materials and resources are well-structured and easy to follow
Prerequisites include basic programming skills and probability theory knowledge

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Reviews summary

Well-received medical imaging course

Learners say this well-structured course on deep learning for medical image analysis is well-received. With engaging lectures and examples, even beginners find this easy to understand. Students especially appreciate the instructor's clear and detailed teaching style.
Suitable for learners of all levels.
"Loved the course. It was easy to learn as Iam beginner"
"Easy to understand for beginners."
"I am a total beginner and the explanations are great"
Mostly positive reviews.
"amazing I've learnt so much so far"
"Very insightful."
"Absolutely Amazing! Love how Sal is so detailed"
"perfect match for me and best decission"
Course is well-organized and easy to follow.
"It was great course for beginners. simple to follow"
"I like the way the course is set up."
"Sal Jade teaches every aspect of the deck, leaving nothing out"
Instructor is clear and detailed.
"The course is good, profound knowledge."
"it is absolutely great!"
"Sal's background as a teacher really shows"
"I appreciate the instructors intuitiveness and incredible amount of knowledge."
Informative and engaging content.
"easy to understand and lots of examples"
"very interesting, easy to follow, easy to understand"
"less things to read and many lectures as videos"
"Super informative AND actually taught from the heart"

Activities

Be better prepared before your course. Deepen your understanding during and after it. Supplement your coursework and achieve mastery of the topics covered in Deep Learning with PyTorch for Medical Image Analysis with these activities:
Study Group
Collaborate with peers to discuss course material, ask questions, and reinforce your understanding.
Show steps
  • Form a study group with 2-3 classmates.
  • Meet regularly to discuss course content and assignments.
  • Take turns presenting key concepts to the group.
Organize study materials
Organizing notes, assignments, and quizzes helps you prepare for the course.
Show steps
  • Gather course materials including notes, assignments, and quizzes
  • Organize materials chronologically by topic or date
  • Review materials to familiarize yourself with key concepts
Organize study groups or discussion forums with classmates
Engaging with peers will enhance your understanding through shared perspectives, discussions, and collaborative problem-solving.
Show steps
  • Connect with fellow students taking the course
  • Establish regular meetings for group discussions or study sessions
14 other activities
Expand to see all activities and additional details
Show all 17 activities
Foundations of Artificial Neural Networks
Review the foundational concepts of ANNs to help solidify and build upon your understanding of the course materials.
Show steps
  • Read Chapters 1-3 of the book.
  • Take notes on the key concepts and definitions.
  • Complete the practice exercises at the end of each chapter.
Revisit the basics of NumPy
Familiarizing yourself with NumPy's core concepts and operations will enhance your understanding of the course material.
Browse courses on NumPy
Show steps
  • Review the documentation on NumPy's website
  • Work through a tutorial or online course on NumPy
PyTorch Tutorial for Beginners
Follow a guided tutorial to gain hands-on experience with PyTorch, the framework used in the course.
Browse courses on PyTorch
Show steps
  • Find a beginner-friendly PyTorch tutorial online.
  • Follow the tutorial step-by-step.
  • Run the code examples provided in the tutorial.
Complete practice problems
Practice can help deepen your understanding of concepts and improve your ability to apply them.
Show steps
  • Identify areas where you need more practice
  • Find practice problems online or in textbooks
  • Set aside time to work on practice problems
  • Review your answers and identify areas for improvement
Medical Image Classification Practice
Practice classifying medical images to strengthen your understanding of the techniques used in the course.
Browse courses on Image Recognition
Show steps
  • Find a dataset of medical images.
  • Load the dataset into your preferred programming environment.
  • Build a model to classify the images.
Practice building and training simple neural networks
Hands-on practice in constructing and training neural networks will solidify your grasp of the fundamental principles.
Browse courses on Neural Networks
Show steps
  • Follow along with the course tutorials on building and training neural networks in PyTorch
  • Work through additional exercises or projects involving neural network development
Develop a summary or tutorial on a specific course topic
Creating content will reinforce your understanding, improve your communication skills, and potentially benefit others.
Show steps
  • Choose a specific topic from the course material
  • Research and gather information on the topic
  • Organize and write your summary or tutorial
Connect with experienced professionals in the field of medical imaging
Seeking guidance from experts will provide valuable insights, expand your network, and facilitate your professional growth.
Show steps
  • Attend industry events or conferences related to medical imaging
  • Reach out to professors, researchers, or practitioners in the field
Cancer Segmentation Project
Develop a model to segment cancer in medical images, applying the techniques learned in the course.
Show steps
  • Gather a dataset of medical images containing cancer.
  • Preprocess the images to prepare them for training.
  • Train a model to segment the cancer.
  • Evaluate the performance of the model.
Explore advanced topics in PyTorch-Lightning
Delving deeper into PyTorch-Lightning's capabilities will equip you with tools for efficient and effective model training.
Show steps
  • Review the official PyTorch-Lightning documentation
  • Follow along with tutorials or workshops on advanced PyTorch-Lightning techniques
Contribute to PyTorch
Engage with the PyTorch community and contribute to its development.
Browse courses on PyTorch
Show steps
  • Find an issue or feature request on the PyTorch GitHub repository.
  • Fork the repository and make changes to address the issue or implement the feature.
  • Create a pull request to merge your changes back into the main branch.
Participate in hackathons or competitions focused on medical imaging
Engaging in competitive events will challenge your knowledge, foster teamwork, and expose you to cutting-edge advancements.
Show steps
  • Research and identify upcoming hackathons or competitions
  • Form a team or collaborate with other participants
  • Develop and submit a project that addresses the competition's goals
Volunteer at a medical imaging facility or research center
Practical experience in a medical imaging setting will provide valuable context and insights beyond the classroom.
Browse courses on Medical Imaging
Show steps
  • Identify local medical imaging facilities or research centers
  • Inquire about volunteer opportunities and requirements
  • Attend volunteer training and orientations
Develop a medical imaging project using the course concepts
Applying your acquired knowledge to a real-world project will enhance your comprehension and practical skills.
Browse courses on Medical Imaging
Show steps
  • Identify a medical imaging problem or dataset to work on
  • Design and implement a deep learning model to address the problem
  • Train and evaluate the model using the provided dataset

Career center

Learners who complete Deep Learning with PyTorch for Medical Image Analysis will develop knowledge and skills that may be useful to these careers:
Machine Learning Engineer
A Machine Learning Engineer designs, builds, tests, deploys, manages, and maintains machine learning models. This course can help you develop the skills needed to build and deploy machine learning models for medical image analysis. The course covers topics such as machine learning theory, deep learning, and medical imaging, which are all essential for a Machine Learning Engineer working in the field of medical image analysis.
Medical Imaging Analyst
A Medical Imaging Analyst designs, builds, tests, deploys, manages, and maintains medical imaging software and systems. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for developing and maintaining medical imaging software and systems. The course also covers topics such as data formats in medical imaging, which is important for understanding how medical images are stored and processed.
Data Scientist
A Data Scientist analyzes data to extract insights and build predictive models. This course can help you develop the skills needed to analyze medical imaging data. The course covers topics such as data preprocessing, feature engineering, and model evaluation, which are all essential for a Data Scientist working in the field of medical image analysis.
Medical Physicist
A Medical Physicist applies the principles of physics to medicine. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for developing and maintaining medical imaging equipment. The course also covers topics such as radiation safety and quality assurance, which are important for ensuring the safe and effective use of medical imaging equipment.
Radiologist
A Radiologist interprets medical images to diagnose and treat diseases. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for interpreting medical images more accurately. The course also covers topics such as anatomy and physiology, which are important for understanding the human body and how it functions.
Ultrasound Technologist
An Ultrasound Technologist performs ultrasound procedures and analyzes the results. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for performing ultrasound procedures more accurately. The course also covers topics such as anatomy and physiology, which are important for understanding the human body and how it functions.
Radiation Therapist
A Radiation Therapist delivers radiation therapy to patients with cancer. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for planning and delivering radiation therapy more accurately. The course also covers topics such as radiation safety and quality assurance, which are important for ensuring the safe and effective use of radiation therapy equipment.
Nuclear Medicine Technologist
A Nuclear Medicine Technologist performs nuclear medicine procedures and analyzes the results. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for performing nuclear medicine procedures more accurately. The course also covers topics such as radiation safety and quality assurance, which are important for ensuring the safe and effective use of nuclear medicine equipment.
Biomedical Engineer
A Biomedical Engineer designs, builds, tests, deploys, manages, and maintains medical devices and systems. This course can help you develop the skills needed to design and develop medical imaging devices and systems. The course covers topics such as medical imaging, signal processing, and embedded systems, which are all essential for a Biomedical Engineer working in the field of medical image analysis.
Healthcare Consultant
A Healthcare Consultant provides consulting services to healthcare providers. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for providing consulting services to healthcare providers on the use of medical imaging. The course also covers topics such as healthcare economics and policy, which are important for understanding the healthcare industry and how to provide consulting services to healthcare providers.
Medical Device Sales Representative
A Medical Device Sales Representative sells medical devices and systems to healthcare providers. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for selling medical imaging devices and systems more effectively. The course also covers topics such as healthcare economics and marketing, which are important for understanding the healthcare industry and how to market medical devices and systems.
Clinical Research Coordinator
A Clinical Research Coordinator manages clinical trials. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for designing and managing clinical trials that involve medical imaging. The course also covers topics such as clinical trial design and data management, which are important for understanding how to conduct clinical trials and manage clinical data.
Science Writer
A Science Writer writes about science and technology for a variety of audiences. This course can help you gain knowledge about the underlying principles of medical imaging, which could be helpful for writing about medical imaging for a variety of audiences. The course also covers topics such as scientific writing and journalism, which are important for understanding how to write about science and technology.
Patent Attorney
A Patent Attorney prepares and prosecutes patent applications. This course may be useful for understanding the underlying principles of medical imaging, which could be helpful for preparing and prosecuting patent applications for medical imaging devices and systems.
Technical Writer
A Technical Writer writes technical documentation. This course may be useful for understanding the underlying principles of medical imaging, which could be helpful for writing technical documentation for medical imaging devices and systems.

Reading list

We've selected nine books that we think will supplement your learning. Use these to develop background knowledge, enrich your coursework, and gain a deeper understanding of the topics covered in Deep Learning with PyTorch for Medical Image Analysis.
Provides valuable background on Deep Learning for Medical Image Analysis and theoretical deep learning knowledge for this course.
Provides additional insight into machine learning theory and techniques that are mentioned in this course.
Provides an accessible introduction to the PyTorch deep learning framework, which is used in this course.
Provides an overview of interpretability techniques, a topic covered in this course.
Covers the fundamentals of computer vision algorithms, which are used in medical image analysis tasks.
This online course provides a practical introduction to deep learning and good resource for those who want to get started with deep learning projects.

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