Deep Learning Explained: An Insight into Drug Discovery & Medical Imaging

There has been an exponential growth of data sets that measure cellular biology & the activity of compounds over the last 5+ years; enough to feed and encourage the use of Machine Learning algorithms such as that of Deep Learning (DL). Whilst gaining impressive traction across a range of applications, DL is well known for its remarkable progress in image recognition.

Specific to pharmaceutical research, Deep Learning provides an ability to mine through extensive biomedical data sets and is paving the way toward alleviating the low success rate in pharmaceutical R&D as well as shortening the tunnel process of drug development - leading to faster medicinal solutions from the very first diagnosis of disease. This blog, part of the DL Explained Series, explains some of the key areas of DL used in the advancement of drug discovery alongside video presentations and detailed diagrams.  

TL;DR

• Robust Deep Image Embeddings uses visible cell proteins & fluorescent dye to record abnormalities
• CNN receive information from input layers on images and pass it through multiple convolution layers which contain 2D feature maps
• Metric Learning is also used, utilising a triplet network. Rather than trying to determine the class that an object falls into, the network learns a way of representing the distance between the embeddings on medical imaging
• Watch a video presentation of how DL is used in Medical Imaging here

Read the full article here: https://blog.re-work.co/how-deep-learning-is-used-in-medical-imaging/