explanation
PyTorch Deep Learning for Medical Image Analysis is a training course on medical image analysis using the Torchfoot machine learning library and deep learning techniques. Carefully analyzing medical images and adopting appropriate treatment methods based on the information obtained and the doctor’s observations is one of the most important tasks of a doctor. Machine-based learning systems are an emerging technology that can help the medical community analyze images and reduce medical errors. Machine learning systems are used in a variety of fields, including cancer diagnosis and segmentation, pneumonia classification, and heart disease diagnosis such as heart rhythm dyssynchrony.
All courses in this curriculum are divided into two parts: theory and practice. In the practice part, you develop and code step by step with the instructor. Machine learning and artificial intelligence in medicine have a long way to go and there are many job opportunities and potential for professionals in this field.
What to Learn in Deep Learning with PyTorch for Medical Image Analysis
- NumPy and its applications in machine learning and deep learning
- Theory and foundation of machine learning-based systems
- Basic data testing and validation
- Machine learning algorithms and model evaluation
- Building tensors in Pythorch
- Convolutional Neural Network
- Knowledge of CT scan images and how to interpret and analyze them.
- Interpretability of network decisions
- Knowledge of cancer and tumor staging process
- Work with a variety of formats and data in medical imaging
- Building an artificial neural network using torch legs
- Work with 2D and 3D data and images
- Automatic diagnosis and classification of cancer, progress monitoring
Course specifications
publisher: Udemy
teacher: Sergios Gattidis, Marcel Prue, Jose Portilla and Tobias Hepp
Language:English
Level: Advanced
Number of classes: 87
Duration: 12 hours
2021/11 Course Topics
Deep Learning Prerequisites with PyTorch for Medical Image Analysis
Understanding of basic Python topics (data types, loops, functions) is also recommended for Python OOP.
Ideally PyTorch, but not strictly necessary.
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Video introducing deep learning using PyTorch for medical image analysis
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