How Artificial Intelligence can help with Cancer Image Translation | AI in Medical Imaging
We developed an AI component that translates a medical image from one domain to another using AI Style Transfer Technique. The goal of the component is to enable re-use of existing software components and machine learning models that are employed to process a specific type of images i.e. images of tissues stained with one type of reagent across all other reagents.
Challenge: The customer required to find a way to apply the disease detection classifier trained on a specific domain of stained tissue images to other images of tissues stained by the different reagent. One of the biggest challenges of this task is the absence of paired images that allow one-to-one comparison e.g. black and white image to color image. Potential solutions include but are not limited to the creation of a new classifier and processing tool-set for each type of stain reagent. However, all these and other available alternatives are cost-intensive and time-consuming. Therefore, the customer decided to commission AI Superior to perform research and development activities to come up with the most appropriate solution for the task.
Solution by AI Superior
Solution by AI Superior: AI Superior helped the customer to evaluate a set of state-of-the-art approaches applied to the histological images in a very short period. Among different learning methods, AI Superior also employed Generative Adversarial Networks (GAN) to do the unpaired image-to-image translation. GAN consists of a couple or more (depending on architecture) deep learning models. It focuses on excelling of image generation task that produces images visually similar to the input training set of images. In addition to the framework that allows translating images into the required domain AI Superior created an interactive visualization tool to validate the quality of generated images.
Outcome and Implications
Outcome and Implications: The software framework for image-to-image translation and the interactive visualization tool allowed the customer to significantly cut the time and costs required to process and classify images of tissues stained with different types of reagents.