Deep Learning and Biomedical Data

This post will discuss what deep learning is and its application to biomedical data

Photo Source: https://robots.net/ai/artificial-intelligence-in-medicine/

A neural network is a machine learning framework that attempts to mimic the learning pattern of natural biological neural networks aka the human brain; therefore, it is designed to enable machines to solve problems humans can solve. Similar to how a biological neural network consists of interconnected neurons with axons that receive inputs and dendrites that produce outputs, an neural network consists of units called perceptrons or neurons that also receive inputs and produce outputs through nodes (there are potentially hidden nodes in between). Neural networks can be used with regression and classification problems, pattern recognition and detection.

Vikas Kumar Sharma, 2018

Unlike a traditional machine learning algorithm, which requires extensive domain expertise and human intervention (which determines what it is designed to do), deep learning, like neural networks, do not. Rather, neural networks can represent abstract concepts without much intervention.

Neural Network applications in medicine

Neural networks have been used for a variety of biomedical data. Here are a few examples:

• Predict DNA-protein binding

• DNA sequence classification

• Digital pathology identification:

  • Microscopy Image Classification

  • Detect and Classify COVID-19 from X-Ray Images