Diagnosing Cancer with Deep Learning and GPUs

Published February 23, 2016

Using GPU-accelerated deep learning, researchers at The Chinese University of Hong Kong pushed the boundaries of cancer image analysis in a way that could one day save physicians and patients precious time.

The team used a TITAN X GPU to win the 2015 Gland Segmentation Challenge held at the Medical Image Computing and Computer conference, the world’s leading conference on medical imaging.

Traditionally, pathologists diagnose cancer by looking for abnormalities in tumor tissue and cells under a microscope, but it’s a time-consuming process that is open to error.

An overview of the team’s proposed framework

The research team trained their deep convolutional neural network on a set of images of known abnormalities. They then used this training for segmenting individual glands from tissues to make it easier to distinguish individual cells, determine their size, shape and location relative to other cells. By calculating these measurements, pathologists can determine the likelihood of malignancy.

“Training with GPUs was 100 times faster than with CPUs,” said Hao Chen, a third-year Ph.D. student and member of the team that developed the solution. “That speed is going to become even more important as we advance our work.”

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