Developing a Multimodal Model for Chest Disease Detection Using Radiology Images and Text Data

This Omdena Local Chapter Challenge runs for 7 weeks and is a unique experience to try and grow your skills in a collaborative and safe environment with a diverse mix of people from all over the world. 

You will work on solving a local problem, initiated by Berlin, Germany Local Chapter, Silicon Valley, USA Chapter.

The problem

Chest diseases are prevalent in countries, and this methodology will help us use two modalities to diagnose accurately and provide richer contextual information leading to better patient outcomes.

The goals

Learn about Multimodal models. How to build them. Become more familiar with NLP and or Computer Vision. This project touches on both topics and more.

Why join? The uniqueness of Omdena Local Chapter Challenges

Omdena Local Chapter Challenges are not a competition or hackathon but a real-world project that will grow your experience to a new level.

A unique learning experience with the potential to make an impact through the outcome of the project. You will go through an entire data science project lifecycle. This covers problem scoping, data collection, and preparation, as well as modeling for deployment.

And the best part is that you will join the global and collaborative community of Omdena with tons of benefits to accelerate your career.

Read more on how Omdena´s Local Chapters work