Current clinical approaches like intraoperative pathology have not solved the persistent problem of incomplete tumor removal. Several technologies have been tried over the past 20 years, but there is currently no existing technology that can deliver the technical performance needed to fully address this problem and “survive in the wild.” In the MAGIC-SCAN project, we will introduce key innovations in microscopy, sample automation, cyber-infrastructure, ML model co-design & training on petascale data, practical & rapid ML model deployment, and cancer detection and visualization. We will take a human-centered approach to innovation, design and development, involving end-users and stakeholders in every aspect of the project to accomplish trustworthy, practical, capable, and cost-conscious product design that optimizes benefits to physicians, payers, and patients.

We will develop the world’s fastest high-resolution tissue scanner and use it to (i) automatically prepare, handle, and scan the complete surface of removed cancerous organs at 0.5 μm resolution, (ii) detect the presence of any residual cancer cells, and (iii) map their location on the specimen surface for surgeon visualization in the operating room within 15 minutes. Two complementary concepts, each representing fundamental scientific and technical advances in their respective fields, comprise our human-centered design. The first, MAGIC-SCAN (Machine-learning Assisted Gigantic Image Cancer margin SCANner) combines extreme-field-of-view optical sectioning and super-resolution structured illumination microscopy (OS-SR-SIM) to obtain very high-speed virtual pathology imaging of cancer tumor margin surfaces at 2x the resolution allowed by diffraction.

To accomplish the cancer classification goals of an end-to-end solution in ≤ 10 minutes, both practically and economically, we will develop the companion concept, FASTMAP (Fast, Accelerated Support for Training MAchine learning models on Petascale data). FASTMAP is a human-centered approach that addresses the iterative nature of the creation-evaluation process of ML cancer-classification models by implementing a high-performance computing cyber-infrastructure capable of developing and training new, accurate models over petascale data on a scale of days. We address this challenge using data management and processing techniques developed over almost two decades of high-performance computing research that are deployed in the latest exascale computing environments. This approach will be complemented by novel strategies for lightning-fast image processing and ML inference at the edge,

We seek a highly skilled and experienced personnel to join our dynamic team and build this cutting-edge device. Below are our current open positions. Click on the title for more information or to apply.