As part of my CMPT 340: Biomedical Computing course at SFU, I worked on a team project to build a web app that displays medical imaging results generated by a machine learning model.

My main contributions focused on integrating the backend and frontend, ensuring the app was functional, user-friendly, and deployable across different environments. I set up a Docker environment to make development and deployment consistent for all team members.

On the frontend, I designed the interface using Figma and implemented it with HTML, TailwindCSS, and JavaScript, creating a clean and responsive layout for users to upload files and view results.

On the backend, I built the Flask server in Python to handle HTTP requests, manage file uploads, and communicate with the trained ML model. I created API routes that processed .dicom files, ran predictions, and returned image masks and bounding box data to the frontend for display. I also handled the data flow between the components, ensuring that the machine learning outputs were visualized correctly in the browser.

Here is a link to a YouTube video demoing the result:

The project code can be found here