Intel helped researchers develop an AI-based model that detects breast cancer more accurately

Intel helped Dr. Das and Dr. Nair develop an AI-based model called NAS-SGAN, which can distinguish different breast cancer grades more accurately

Two researchers, Dr. Madhu Nair from the Artificial Intelligence & Computer Vision Lab, Cochin University of Science and Technology (CUSAT), and Dr. Asha Das from Union Christian College, India have developed a novel approach to help diagnose breast cancer in its early stages.

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Dr. Das and Dr. Nair have created a model called NAS-SGAN, which can distinguish the different cancer grades. Their model leverages deep learning and uses labelled and unlabelled images together to achieve high accuracy.

They have worked with Intel on this project. For the best results, the deep learning solution which they have used needed to process high-resolution images, but was limited by the GPU’s inability to hold the entire AI model in memory. To help overcome this, Intel helped the researchers with a technology architecture based on its Xeon Scalable processors.

NAS-GAN works in two phases: A GAN (Generative Adversarial Network) is used to create images that are indistinguishable from genuine histopathological images. The GAN is trained using unlabelled images, which are relatively easy to obtain.

The new images are used to help the solution understand the data distribution. The GAN discriminator is then trained with the labelled images to predict the cancer grades. The NAS-SGAN approach uses four servers based on Intel Xeon Scalable processors which train the solution in parallel, with 192GB of memory per server. Intel Optimization for TensorFlow meanwhile makes it easy to use acceleration features in the processors.

Dr. Das and Dr. Nair compared the performance of NAS-SGAN with 10 other GAN algorithms used to detect breast cancer. The NAS-SGAN algorithm addresses the shortcomings of other GAN models for breast cancer screening by adding the ability to grade cancer images.