Deep Learning with PyTorch for Medical Image Analysis
4.0 /5- Offered byUDEMY
Deep Learning with PyTorch for Medical Image Analysis at UDEMY Overview
Duration | 12 hours |
Total fee | ₹3,499 |
Mode of learning | Online |
Credential | Certificate |
Deep Learning with PyTorch for Medical Image Analysis at UDEMY Highlights
- Earn a certificate of completion from Udemy
- Learn from 7 downloadable resources & 5 articles
- Get full lifetime access of the course material
- Comes with 30 days money back guarantee
Deep Learning with PyTorch for Medical Image Analysis at UDEMY Course details
- For Python developers and Machine Learning engineers who want to learn how to tackle real world problems occurring on a daily basis in the field of medical imaging with the help of Deep Convolutional Neural Networks.
- For Everybody who wants to learn more about the joint field of AI and Medical Imaging & how it works
- For Developers familiar with basic Deep Learning knowledge who want to apply their skills to more than toy problems
- For Medical professionals interested in how AI actually works in medicine
- 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
- 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
- This course provides unique knowledge on the application of deep learning to highly complex and non-standard (medical) problems (in 2D and 3D)
Deep Learning with PyTorch for Medical Image Analysis at UDEMY Curriculum
Introduction
Course Overview Lecture
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
Overview: X-RAY
Overview: CT
Overview: MRI
Overview: 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
Deep Learning with PyTorch for Medical Image Analysis at UDEMY Faculty details
Deep Learning with PyTorch for Medical Image Analysis at UDEMY Entry Requirements
- Prerequisite: Understanding of Python Basic Topics (data types,loops,functions) also Python OOP recommended
- Not mentioned
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