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Using Deep Neural Network Acceleration for Image Analysis in Drug Discovery

Wednesday, May 23rd, 2018

What’s New: Intel collaborates with Novartis* on the use of deep neural networks (DNN) to accelerate high content screening – a key element of early drug discovery. The collaboration team cut time to train image analysis models from 11 hours to 31 minutes – an improvement of greater than 20 times1.

Collaboration team members from Novartis and Intel used eight CPU-based servers, a high-speed fabric interconnect and optimized TensorFlow to achieve the improvement in time needed to process a dataset of 10K images.

Why It’s Important: High content screening of cellular phenotypes is a fundamental tool supporting early drug discovery. The term “high content” signifies the rich set of thousands of pre-defined features (such as size, shape, texture) that are extracted from images using classical image-processing techniques. High content screening allows analysis of microscopic images to study the effects of thousands of genetic or chemical treatments on different cell cultures.

The promise of deep learning is that relevant image features that can distinguish one treatment from another are “automatically” learned from the data. By applying deep neural network acceleration, biologists and data scientists at Intel and Novartis hope to speed up the analysis of high content imaging screens. In this joint work, the team is focusing on whole microscopy images as opposed to using a separate process to identify each cell in an image first. Whole microscopy images can be much larger than those typically found in deep learning datasets. For example, the images used in this evaluation are more than 26 times larger than images typically used from the well-known ImageNet* dataset of animals, objects and scenes.

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ClioSoft at DAC



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