Course Content
Course Introduction
<div class="flex flex-col text-sm pb-25"><section class="text-token-text-primary w-full focus:outline-none [--shadow-height:45px] has-data-writing-block:pointer-events-none has-data-writing-block:-mt-(--shadow-height) has-data-writing-block:pt-(--shadow-height) [&amp;:has([data-writing-block])&gt;*]:pointer-events-auto scroll-mt-[calc(var(--header-height)+min(200px,max(70px,20svh)))]" dir="auto" data-turn-id="request-WEB:8782c032-bcff-4445-86ad-fbc12be09cef-0" data-testid="conversation-turn-4" data-scroll-anchor="true" data-turn="assistant"> <div class="text-base my-auto mx-auto pb-10 [--thread-content-margin:var(--thread-content-margin-xs,calc(var(--spacing)*4))] @w-sm/main:[--thread-content-margin:var(--thread-content-margin-sm,calc(var(--spacing)*6))] @w-lg/main:[--thread-content-margin:var(--thread-content-margin-lg,calc(var(--spacing)*16))] px-(--thread-content-margin)"> <div class="[--thread-content-max-width:40rem] @w-lg/main:[--thread-content-max-width:48rem] mx-auto max-w-(--thread-content-max-width) flex-1 group/turn-messages focus-visible:outline-hidden relative flex w-full min-w-0 flex-col agent-turn"> <div class="flex max-w-full flex-col gap-4 grow"> <div class="min-h-8 text-message relative flex w-full flex-col items-end gap-2 text-start break-words whitespace-normal outline-none keyboard-focused:focus-ring [.text-message+&amp;]:mt-1" dir="auto" data-message-author-role="assistant" data-message-id="20754dc1-7377-440b-828e-bdc1f94d71b3" data-message-model-slug="gpt-5-4-thinking" data-turn-start-message="true"> <div class="flex w-full flex-col gap-1 empty:hidden"> <div class="streaming-animation markdown prose dark:prose-invert w-full wrap-break-word dark markdown-new-styling"> <p data-start="0" data-end="73" data-is-last-node="" data-is-only-node="">Review the content and methodologies that will be applied in this course...</p> </div> </div> </div> </div> </div> </div> </section></div> <div class="pointer-events-none h-px w-px absolute bottom-0" aria-hidden="true" data-edge="true"></div>
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Welcome and Course Description
05:43
Instructions for Using Google Colab.
04:25
Module 1: Introduction to Radiomics and Properties of Medical Images
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Lesson 1: Introduction to Radiomics
20:03
Hands-on 1: Introducción a programación en Python (primera parte)
59:44
Hands-on 2: Introduction to Python Programming (Part 2)
00:00
Lesson 2: Properties of Medical Images
00:00
Hands-on 3: Basic Image Operations in Python
00:00
Hands-on 4: Medical Image Processing in Python
00:00
Lesson 3: Feature Analysis
38:37
Quiz: Module 1
00:30:00
Module 2: Preprocessing and Feature Extraction
0/7
Lesson 4: Radiomics Workflow
29:50
Lesson 5: Image Preprocessing
25:58
Lesson 6: Medical Image Segmentation Techniques
25:48
Hands-on 5: Semi-Automatic Medical Image Segmentation
40:47
Hands-on 6: Automatic Segmentation with Deep Learning
00:00
Hands-on 7: Feature Extraction with PyRadiomics
00:00
Quiz: Module 2
Module 3: Statistical Modeling
0/8
Lesson 7: Statistical Modeling (Part 1)
40:03
Lesson 8: Statistical Modeling (Part 2)
38:32
Hands-on 8: Database Consolidation and Construction
00:00
Hands-on 9: Data Cleaning
00:00
Lesson 9: Variable Selection Techniques
00:00
Hands-on 10: Exploratory Data Analysis and Variable Selection (Part 1)
00:00
Hands-on 11: Exploratory Data Analysis and Variable Selection (Part 2)
00:00
Quiz: Module 3
00:20:00
Módulo 4: Machine Learning en Radiomics
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Lesson 10: Fundamentals of Machine Learning Applied to Radiomics (Part 1)
32:14
Lesson 11: Fundamentals of Machine Learning Applied to Radiomics (Part 2)
29:00
Hands-on 12: Building Machine Learning Models for Radiomics (Part 1)
00:00
Hands-on 13: Building Machine Learning Models for Radiomics (Part 2)
00:00
Lesson 12: Fundamentals of Deep Learning Applied to Radiomics
00:00
Lesson 13: Reproducibility and Transparency in Radiomics Studies
00:00
Quiz: Module 4
00:30:00
Quiz Module 4
00:30:00
Closing remarks
01:35
Radiomics from zero
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