XAI Heatmap Generation

What you'll build

This project involves creating a Streamlit application that demonstrates a deep learning model's ability to localize cancer in lung images using explainable AI (XAI) techniques. The application will display medical images (e.g., X-rays or CT scans) and overlay them with heatmaps generated by the model to show the regions of interest. The heatmap will be generated using gradient-based methods like Grad-CAM, which provide insights into how the model makes its predictions. The app will be designed to be user-friendly, with clear visualizations and explanations, making it suitable for demonstrating the model's functionality to both technical and non-technical audiences.

What you'll learn

XAI concepts
Heatmap generation
Overlaying heatmaps on images

Roadmap

16 steps · 16 tasks

XAI Heatmap Generation

Install Dependencies

Streamlit App Structure Setup

Prepare Medical Images and Data

Load a pre-trained deep learning model for lung cancer detection

Implement Grad-CAM or another XAI method to generate heatmaps

Create a function to overlay the heatmap on the original medical image

Build the Streamlit UI with image display and explanation features

Add functionality to load and process images

Implement the model prediction and heatmap generation

Display the original image and heatmap side-by-side

Add explanatory text about how the model works

Test the application with sample data

Refine the UI for better user experience

Add error handling and loading states

Document the code and add comments

Prepare the app for deployment or sharing