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    Deep Learning with PyTorch for Medical Image Analysis

    BY
    Udemy

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    Mode

    Online

    Fees

    ₹ 599 4099

    Quick Facts

    particular details
    Medium of instructions English
    Mode of learning Self study
    Mode of Delivery Video and Text Based

    Course and certificate fees

    Fees information
    ₹ 599  ₹4,099
    certificate availability

    Yes

    certificate providing authority

    Udemy

    The syllabus

    Introduction

    • COURSE OVERVIEW LECTURE - PLEASE DO NOT SKIP!
    • Link to Download the Course Files
    • Installation and Environment Setup
    • 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 Metric

    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

    • Introduction
    • Overview: X-RAY
    • Overview: CT
    • Overview: MRI
    • Overview: PET
    • Recap: Medical Imaging

    Data Formats in Medical Imaging

    • Introduction
    • 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

    Pneumonia - Classification

    • Introduction
    • Preprocessing
    • Train-01-Data-Loading
    • Train-02-Model-Creation
    • Train-03-Trainer
    • Train-04-Evaluation
    • Interpretability

    Cardiac-Detection

    • Introduction
    • Preprocessing
    • Dataset-Part-1
    • Dataset-Part-2
    • Train-01-Data-Loading
    • Train-02-Model-Creation
    • Train-03-Evaluation

    Atrium-Segmentation

    • Introduction
    • Preprocessing-01-Visualization
    • Preprocessing-02-Processing
    • Dataset-01-Dataset-Creation
    • Dataset-02-Dataset-Validation
    • UNet
    • Train-01-Data-Loading-and-Loss
    • Train-02-Model-Creation
    • Train-03-Evaluation

    Capstone-Project: Lung Tumor Segmentation

    • Introduction
    • Overview
    • Oversampling
    • Hint - RuntimeError: expected scalar type Double but found Float
    • Discussion

    3D Liver and Liver Tumor Segmentation

    • Introduction
    • Data-Visualization
    • Model
    • Train-01-TorchIO-Dataset
    • Train-02-Model-Creation
    • Train-03-Evaluation

    BONUS SECTION: THANK YOU!

    • BONUS LECTURE

    Instructors

    Mr Jose Portilla
    Head of Data Science
    Udemy

    Other Bachelors, M.S

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