  Accurate intraoperative diagnosis is essential for providing safe and
effective care during brain tumor surgery. Our standard-of-care diagnostic
methods are time, resource, and labor intensive, which restricts access to
optimal surgical treatments. To address these limitations, we propose an
alternative workflow that combines stimulated Raman histology (SRH), a rapid
optical imaging method, with deep learning-based automated interpretation of
SRH images for intraoperative brain tumor diagnosis and real-time surgical
decision support. Here, we present OpenSRH, the first public dataset of
clinical SRH images from 300+ brain tumors patients and 1300+ unique whole
slide optical images. OpenSRH contains data from the most common brain tumors
diagnoses, full pathologic annotations, whole slide tumor segmentations, raw
and processed optical imaging data for end-to-end model development and
validation. We provide a framework for patch-based whole slide SRH
classification and inference using weak (i.e. patient-level) diagnostic labels.
Finally, we benchmark two computer vision tasks: multiclass histologic brain
tumor classification and patch-based contrastive representation learning. We
hope OpenSRH will facilitate the clinical translation of rapid optical imaging
and real-time ML-based surgical decision support in order to improve the
access, safety, and efficacy of cancer surgery in the era of precision
medicine. Dataset access, code, and benchmarks are available at
opensrh.mlins.org.
