Using CUDA and Machine Learning to Detect Colon Cancer

As part of the GlaS@MICCAI2015 colon gland segmentation challenge, a team of researchers introduced a machine learning-based algorithm to segment glands in tissue of benign and malignant colorectal cancer.

The variability of glandular structures in biological tissue poses a challenge to automated analysis of histopathology slides. It has become a key requirement to  quantitative morphology assessment and supporting cancer grading.

Using GPUs to detect colon cancer
Qualitative segmentation results on images: Segmentation (blue outline) and ground truth (green outline), false negative pixels are cyan, and false positive pixels are yellow

Using GPUs, CUDA, and Pylearn2 — a machine learning library built on top of Theano — the team trained their two deep convolution neural networks on a set of 125,000 images and achieved a classification accuracy of 98% and 94%, making use of the inherent capability of the system to distinguish between benign and malignant tissue.

In related news, the NVIDIA Foundation recently awarded $200,000 to a team of researchers from the University of Toronto for their GPU-accelerated cancer research by developing a “genetic interpretation engine” – a deep learning method for identifying cancer-causing mutations.

Read the research paper >>

About Brad Nemire

Brad Nemire
Brad Nemire is on the Developer Marketing team and loves reading about all of the fascinating research being done by developers using NVIDIA GPUs. Reach out to Brad on Twitter @BradNemire and let him know how you’re using GPUs to accelerate your research. Brad graduated from San Diego State University and currently resides in San Jose, CA. Follow @BradNemire on Twitter