Radiology and Deep Learning

Radiology and Deep Learning

  • November 27, 2018
Table of Contents

Radiology and Deep Learning

Radiology and DeepLearningDetecting pneumonia opacities from chest X-Ray images using deep learning. One day back in August, I was catching up with my best friend from high school who is now a radiology resident. One thing led to another, and we started talking about our interests in artificial intelligence and machine learning and its possible applications in radiology.

A couple of months after our talk, I stumbled upon a Kaggle challenge hosted by the Radiological Society of North American (RSNA). It was a competition that we could work on together, so I immediately called my friend. Joined by my brother, we formed a team to compete in this Kaggle challenge.

After a month of hard work, we ended up finishing in the top 3%. In this blog post, I’d like to detail what we did during the exciting month. One day back in August, I was catching up with my best friend from high school who is now a radiology resident.

One thing led to another, and we started talking about our interests in artificial intelligence and machine learning and its possible applications in radiology. A couple of months after our talk, I stumbled upon a Kaggle challenge hosted by the Radiological Society of North American (RSNA). It was a competition that we could work on together, so I immediately called my friend.

Joined by my brother, we formed a team to compete in this Kaggle challenge. After a month of hard work, we ended up finishing in the top 3%. In this blog post, I’d like to detail what we did during the exciting month.

Source: medium.com

Share :
comments powered by Disqus

Related Posts

Google: Deep Learning for Electronic Health Records

Google: Deep Learning for Electronic Health Records

When patients get admitted to a hospital, they have many questions about what will happen next. When will I be able to go home? Will I get better?

Read More
The dark side of YouTube

The dark side of YouTube

The YouTube algorithm that I helped build in 2011 still recommends the flat earth theory by the hundreds of millions. This investigation by @RawStory shows some of the real-life consequences of this badly designed AI.

Read More
A Google Brain engineer’s guide to entering AI

A Google Brain engineer’s guide to entering AI

Note that this guide was written in November 2018 to complement an in-depth conversation on the 80,000 Hours Podcast with Catherine Olsson and Daniel Ziegler on how to transition from computer science and software engineering in general into ML engineering, with a focus on alignment and safety. If you like this guide, we’d strongly encourage you to check out the podcast episode where we discuss some of the instructions here, and other relevant advice. Technical AI safety is a multifaceted area of research, with many sub-questions in areas such as reward learning, robustness, and interpretability.

Read More