About Course
Learn the theoretical and practical foundations of radiomics methodologies. By the end of this course, you will be able to implement a complete radiomics project in medical imaging from scratch.
Course Content
Course Introduction
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Welcome and Course Description
05:43Instructions for Using Google Colab.
04:25
Module 1: Introduction to Radiomics and Properties of Medical Images
- 20:03
Hands-on 1: Introducción a programación en Python (primera parte)
59:44Hands-on 2: Introduction to Python Programming (Part 2)
00:00Lesson 2: Properties of Medical Images
38:18Hands-on 3: Basic Image Operations in Python
00:00Hands-on 4: Medical Image Processing in Python
00:00Lesson 3: Feature Analysis
38:37Quiz: Module 1
Module 2: Preprocessing and Feature Extraction
Lesson 4: Radiomics Workflow
29:50Lesson 5: Image Preprocessing
25:58Lesson 6: Medical Image Segmentation Techniques
25:48Hands-on 5: Semi-Automatic Medical Image Segmentation
40:47Hands-on 6: Automatic Segmentation with Deep Learning
00:00Hands-on 7: Feature Extraction with PyRadiomics
00:00Quiz: Module 2
Module 3: Statistical Modeling
Lesson 7: Statistical Modeling (Part 1)
40:03Lesson 8: Statistical Modeling (Part 2)
38:32Hands-on 8: Database Consolidation and Construction
00:00Hands-on 9: Data Cleaning
00:00Lesson 9: Variable Selection Techniques
00:00Hands-on 10: Exploratory Data Analysis and Variable Selection (Part 1)
00:00Hands-on 11: Exploratory Data Analysis and Variable Selection (Part 2)
00:00Quiz: Module 3
Módulo 4: Machine Learning en Radiomics
Lesson 10: Fundamentals of Machine Learning Applied to Radiomics (Part 1)
32:14Lesson 11: Fundamentals of Machine Learning Applied to Radiomics (Part 2)
29:00Hands-on 12: Building Machine Learning Models for Radiomics (Part 1)
00:00Hands-on 13: Building Machine Learning Models for Radiomics (Part 2)
00:00Lesson 12: Fundamentals of Deep Learning Applied to Radiomics
00:00Lesson 13: Reproducibility and Transparency in Radiomics Studies
00:00Quiz: Module 4
Closing remarks
01:35
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