ABSTRACT Label-free optical microscopy has emerged as a promising method for rapid imaging of fresh, unprocessed surgical specimens. Stimulated Raman histology (SRH) – a label-free, non-destructive, high-sensitivity optical imaging method – combined with artificial intelligence (AI) has been used for bedside brain tumor diagnosis, margin delineation, and molecular genetic prediction. Previous AI methods are limited because they rely on weak, slide- or patient-level annotations for model training. Importantly, these annotations fail to capture the cellular complexity and spatial heterogeneity found in diffuse gliomas, the most common and deadly primary brain tumor. A major barrier to advancing the role of label-free optical imaging in diffuse glioma research and patient treatment is developing strategies to allow AI-based computer vision models to learn rich, high- resolution, single-cell optical image features, which allow for a more complete description of the underlying tumor biology. The objective of this research is to determine if an AI-based computer vision system can learn single-cell optical image features. I will (1) detect single cells in diffuse glioma specimens imaged using SRH; (2) optimize learned single-cell features using representative single-cell examples, which we call exemplar learning; and (3) identify patient-level clusters based on optical single-cell features. Successful completion of this proposal will reduce the reliance of AI on weak annotations, and advance the role of AI in diffuse glioma research and treatment via single-cell optical features and patient-level clusters. In the long term, AI methods in this proposal can be integrated with the diagnosis workflow using optical imaging, and provide physicians with additional tools to further differentiate diffuse gliomas, and enable better and more personalized care.

PROJECT NARRATIVE Label-free optical microscopy, combined with artificial intelligence (AI), has emerged as a promising method for the rapid imaging of fresh, unprocessed surgical specimens, bedside brain tumor diagnosis, margin delineation, and molecular genetic prediction. Existing AI methodologies are limited because they rely on weak, slide- or patient-level annotations for model training, and are therefore unable to capture the cellular complexity and spatial heterogeneity found in diffuse gliomas. The objective of this research is to develop an innovative AI-based system to learn single-cell optical image features and identify patient-level clusters, without weak supervision, in order to advance the role of AI in diffuse glioma research and enable better, and more personalized care.