Bootcamp-AI-for-Medical-Imaging

🧠 AI for Medical Imaging Bootcamp

Welcome to the AI for Medical Imaging Bootcamp, a self-paced introduction to building intelligent systems that analyze, interpret, and learn from medical images.
This bootcamp combines foundational AI concepts with hands-on modules that guide you from data preprocessing to segmentation, biomarker discovery, and report generation.

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🎯 Purpose

The goal of this bootcamp is to help you:

• Understand the core principles of AI in medical imaging.
• Learn how to design and implement end-to-end imaging workflows.
• Gain hands-on experience with open-source tools such as PyRadiomics, MERLIN, nnU-Net, and TotalSegmentator.
• Build confidence to contribute to research in the BioIntelligence Lab or launch your own imaging AI projects.

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🧩 Bootcamp Overview

Phase	Focus	Modules	Description
🐍 Module 0 (Optional)	Python Foundations	—	For beginners: complete the Kaggle Python Course.
⚙️ Core Bootcamp	Fundamentals of AI for Medical Imaging	1 – 4	Learn data science, image processing, ML, DL, and segmentation.
🧬 Application Bootcamp	Imaging Biomarkers & Beyond	5	Apply AI techniques to real-world medical imaging workflows.

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⚙️ Core Bootcamp (Modules 1 – 4)

These modules build your foundation in medical imaging AI — from basic data science to segmentation model training.
Each module can be opened directly in Google Colab — just click and start learning.

Module	Description	Link
1. Data Science Foundations	Learn Python, NumPy, and Pandas for data wrangling, visualization, and exploratory analysis.	Open in Colab →
2. Image Processing	Explore image filtering, enhancement, and region-based operations using OpenCV and SimpleITK.	Open in Colab →
3. Machine Learning	Build and evaluate classical ML models using scikit-learn on imaging datasets.	Open in Colab →
4. Introduction to Deep Learning	Understand CNN architectures and how deep networks process images using Keras and fast.ai.	Open in Colab →

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🩻 Application Bootcamp (Modules 5)

This module aims to connect foundational knowledge to a real-world application.

Module	Learning Goal	Link
5. Imaging Biomarkers — From Data to Prediction	Build a complete imaging biomarker pipeline: cohort discovery → data download → segmentation → feature extraction → predictive modeling.	Open in Colab →

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📫 Contact

BioIntelligence Lab — UTHealth Houston
👨‍🏫 Dr. Vishwa S. Parekh
📧 vishwa.s.parekh@uth.tmc.edu
🌐 BioIntelligence-Lab GitHub