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

Tags :
Share :
comments powered by Disqus

Related Posts

Decision Tree in Machine Learning

Decision Tree in Machine Learning

A decision tree is a flowchart-like structure in which each internal node represents a test on a feature (e.g. whether a coin flip comes up heads or tails), each leaf node represents a class label (decision taken after computing all features) and branches represent conjunctions of features that lead to those class labels. The paths from root to leaf represent classification rules. Below diagram illustrate the basic flow of decision tree for decision making with labels (Rain(Yes), No Rain(No)).

Read More
Predictive Scaling for EC2, Powered by Machine Learning

Predictive Scaling for EC2, Powered by Machine Learning

When I look back on the history of AWS and think about the launches that truly signify the fundamentally dynamic, on-demand nature of the cloud, two stand out in my memory: the launch of Amazon EC2 in 2006 and the concurrent launch of CloudWatch Metrics, Auto Scaling, and Elastic Load Balancing in 2009. The first launch provided access to compute power; the second made it possible to use that access to rapidly respond to changes in demand. We have added a multitude of features to all of these services since then, but as far as I am concerned they are still central and fundamental!

Read More
Accurate Online Speaker Diarization with Supervised Learning

Accurate Online Speaker Diarization with Supervised Learning

Speaker diarization, the process of partitioning an audio stream with multiple people into homogeneous segments associated with each individual, is an important part of speech recognition systems. By solving the problem of “who spoke when”, speaker diarization has applications in many important scenarios, such as understanding medical conversations, video captioning and more. However, training these systems with supervised learning methods is challenging — unlike standard supervised classification tasks, a robust diarization model requires the ability to associate new individuals with distinct speech segments that weren’t involved in training.

Read More