I first encountered Heather through her graduate studies, where her groundbreaking work demonstrated new capabilities of artificial intelligence (AI) to reveal molecular driver lesions in cancer from routine H&E-stained tissue pathology slides. Since then, through social media she has become a key interpreter and highly vocal advocate for an exploding field of research that aims to dramatically impact the practice of pathology as we have known it for well over a century. As a computer scientist, her combination of experience with AI algorithms and wide domain knowledge is poised to make pivotal contributions to, among many fields, the emerging field of computational pathology.
Our team was fortunate to work with Heather on a short-term project, where she was a critical contributor to establishing a framework for future studies. She has an unusual ability to demystify, in plain language, new developments in computer science for a wide audience, and I am confident through her work she will influence many teams and help bring to reality the next set of tools to transform pathology.
At CytoVeris, our mission is to use artificial intelligence to power our multi-spectral imaging platform to detect cancer intraoperatively in real-time. Working with Heather and Pixel Scientia Labs was a great choice for us because of her extensive experience in cancer detection, imaging, and machine learning. Based on this background and expertise, she was able to support the team as we worked to empower our imaging solutions through AI and machine learning.
Heather interacts closely with our team and helps enormously in guiding our efforts toward the machine learning architectures that perform best for our system. Her deep understanding of the diverse range of ML architectures and capabilities allows us focus on the data preprocessing, data augmentation, and validation techniques that produce the most robust results given our unique data and challenges.
Heather's knowledge of the current state of the art within the digital pathology field is second to none. Consultation on best practice approaches for deep learning model classification performance and insight into digital research perspectives were very beneficial. Discussions of prior art enabled our team to focus on novel research and refine our current AI methodologies for clinical research.
Pixel Scientia provides valuable insights for Gestalt's image analysis product development. Heather has deep understanding of digital pathology and machine learning and their application to whole slide images. Her in-depth review of the current state of the art research has enabled Gestalt to rapidly focus our machine learning efforts on approaches that are yielding value for our pathologists and their patients.
Heather provided excellent consulting on our clinical AI research. She offered a wealth of knowledge in this domain and introduced us to new techniques to combat the failure modes that we presented. She helped to distill the latest machine learning literature relevant to our queries, and pointed us towards the most applicable and successful methods. Heather provided practical feedback and guidance, which allowed us to quickly take steps in the right direction and improve the performance of our models. These consultation sessions were very effective and I would be delighted to work with Heather again.