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Using GPU-powered deep learning, researchers at the Chinese University of Hong Kong have pushed the boundaries of cancer image analysis in a way that could one day save physicians – and patients – precious time. The team’s work focused on colon cancer – the third most common cancer worldwide – recently took top honors at a challenge contest held at the Medical Image Computing and Computer conference, the world’s leading conference on medical imaging. Pathologists diagnose cancer by looking for abnormalities in tumor tissue and cells. The more abnormal, the more likely the cancer will grow and spread quickly. Traditionally, pathologists do this by examining tissue under a microscope. It’s a time-consuming process that’s open to error.
With GPU-accelerated deep learning, the research team was able to quickly train computers on a relatively small set of images of known abnormalities. The systems 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, pathologist can determine the likelihood of malignancy. “GPUs dramatically sped up training the computers,” 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.”
With GPU-accelerated deep learning, the research team was able to quickly train computers on a relatively small set of images of known abnormalities. The systems 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, pathologist can determine the likelihood of malignancy. “GPUs dramatically sped up training the computers,” 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.”