Automated analysis of pathology images enables quantification of image properties in a systematic way with the ability to learn new features to better distinguish different classes - even beyond the visual capability of human experts.
Machine learning can predict disease biomarkers to provide a better understanding of why drug efficacy varies from patient to patient. It can stratify patients according to risk and identify the best treatment for a particular patient.
Similar algorithms for quantifying images can also track the contributors to climate change. Remote sensing enables us to do this globally and may be one of the keys to mitigating or adapting to this global crisis.
Pathology and remote sensing images are large, multispectral, and heterogeneous. They are time-consuming to annotate and require domain knowledge to effectively model. While the terminology of each discipline is unique, the same algorithms can extract powerful insights to drive impact.
While machine learning certainly can bring new insights, precision, and efficiency, it takes time to build the technology to do it. And the path to a successful solution is generally not linear.
Many companies outsource machine learning projects. While this can be successful for well-defined applications with a limited scope, high impact projects are different. These projects are often part of a company’s core technology, so they prefer to keep the intellectual property development in house. But they may not have navigated the unique complexities of a machine learning project before. We can help you build the technology and your team in house, ensuring a smoother path to success.
Our goal is not just to get you to an ideal solution but to ensure your team also understands how it works so that they can modify it and adapt to new challenges that arise. We will be there to help you navigate these obstacles as needed, but we consider our role most successful if we have transferred this knowledge to your team.
