Your Journey into Imaging AI Starts Here

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Your journey into Imaging AI Starts here

Your Journey into Imaging AI Starts Here

Created by Dr. Sharad Maheshwari, imagingsimplified@gmail.com

This guide provides an interactive overview for radiologists venturing into AI, from curating data to fine-tuning models for clinical use.

The AI Development Workflow

🖥️

1. Curate Data

Gather & anonymize diverse clinical cases.

✏️

2. Annotate

Label images to highlight regions of interest.

🛠️

3. Train Model

Use tools like Python & Hugging Face.

⚙️

4. Fine-Tune

Adapt a pre-trained model for a specific task.

✅

5. Evaluate

Test the model on unseen data for accuracy.

1. Building a High-Quality Dataset

The performance of any model is fundamentally dependent on the quality of its training data. This begins with meticulous annotation and thoughtful dataset construction.

Open-Source Annotation Tools

Your clinical expertise is irreplaceable in labeling images. These tools can help.

3D Slicer

Versatile platform for segmentation and 3D visualization (CT, MRI).

ITK-SNAP

Focused on semi-automatic and manual segmentation of 3D images.

Labelbox & V7

Cloud-based, collaborative platforms that support DICOM formats.

Ideal Dataset Split

A dataset is divided to train, validate, and test the model independently. Hover over the chart for details.

2. Choosing Your Tools & Platforms

With a quality dataset, the next step is selecting the right tools to build, train, and fine-tune your model.

Python Libraries for Imaging AI

Python is the leading language for AI. These libraries are essential for development.

The Hugging Face Ecosystem

Hugging Face democratizes AI by providing a central hub of pre-trained models and tools, which is invaluable for healthcare applications.

Hugging Face Hub

Access thousands of state-of-the-art models to use as a foundation, saving immense time and resources.

AutoTrain: The Game-Changer

An automated tool that lets you train models with a user-friendly interface, no complex code required. Perfect for busy clinicians.

Google Colab: Your Cloud Notebook

A free, cloud-based environment that provides GPU access, making it the perfect platform for running your Python code and training models without needing powerful local hardware.

3. Fine-Tuning a Model in Practice

Let's walk through the conceptual steps of fine-tuning a foundation model to generate chest x-ray reports using Google Colab. Click each step for details.

Key Concepts Glossary

Understanding these terms is crucial for effective model training. Click on a card to flip it and see the definition.

References for Further Learning

Explore these resources to deepen your understanding of the concepts discussed in this guide.

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