Specialization Course in Radiomics
Dare to begin the most comprehensive course you will find on Radiomics and Machine Learning in Radiology for healthcare professionals.
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Course Program
Module 1:
- Welcome and Introduction.
- Google Colab and Hands-on Instructions.
- Class 1: Introduction to Radiomics.
- Hands-on 1: Introduction to Python Programming (Part One)
- Hands-on 2: Introduction to Python Programming (Part Two)
- Class 2: Propiedades de las imágenes Médicas.
- Hands-on 3: Basic Image Operations in Python.
- Hands-on 4: Medical Image Processing in Python.
- Class 3: Feature Analysis.
Module 1 Assessment.
Module 2:
- Class 4: Radiomics Workflow.
- Class 5: Image Preprocessing.
- Class 6: Medical Image Segmentation Techniques.
- Hands-on 5: Semi-automatic Medical Image Segmentation.
- Hands-on 6: Automatic Medical Image Segmentation with Deep Learning.
- Hands-on 7: Feature Extraction with PyRadiomics.
Module 2 Assessment.
Module 3:
- Class 7: Statistical Modeling (Part One).
- Class 8: Statistical Modeling (Part Two).
- Hands-on 8: Database Consolidation and Construction.
- Hands-on 9: Data Cleaning.
- Hands-on 10: Exploratory Data Analysis and Feature Selection (Part One).
- Hands-on 11: Exploratory Data Analysis and Feature Selection (Part Two).
Module 3 Assessment.
Module 4:
- Class 10: Fundamentals of Machine Learning Applied to Radiomics (Part One).
- Class 11: Fundamentals of Machine Learning Applied to Radiomics (Part Two).
- Hands-on 12: Building Machine Learning Models for Radiomics (Part One).
- Hands-on 13: Building Machine Learning Models for Radiomics (Part Two).
- Class 12: Deep Learning Applied to Radiomics.
- Class 13: Reproducibility and Transparency in Radiomics Projects.
Module 4 Assessment.