  Raman spectroscopy, a photonic modality based on the inelastic backscattering
of coherent light, is a valuable asset to the intraoperative sensing space,
offering non-ionizing potential and highly-specific molecular fingerprint-like
spectroscopic signatures that can be used for diagnosis of pathological tissue
in the dynamic surgical field. Though Raman suffers from weakness in intensity,
Surface-Enhanced Raman Spectroscopy (SERS), which uses metal nanostructures to
amplify Raman signals, can achieve detection sensitivities that rival
traditional photonic modalities. In this study, we outline a robotic Raman
system that can reliably pinpoint the location and boundaries of a tumor
embedded in healthy tissue, modeled here as a tissue-mimicking phantom with
selectively infused Gold Nanostar regions. Further, due to the relative dearth
of collected biological SERS or Raman data, we implement transfer learning to
achieve 100% validation classification accuracy for Gold Nanostars compared to
Control Agarose, thus providing a proof-of-concept for Raman-based deep
learning training pipelines. We reconstruct a surgical field of 30x60mm in 10.2
minutes, and achieve 98.2% accuracy, preserving relative measurements between
features in the phantom. We also achieve an 84.3% Intersection-over-Union
score, which is the extent of overlap between the ground truth and predicted
reconstructions. Lastly, we also demonstrate that the Raman system and
classification algorithm do not discern based on sample color, but instead on
presence of SERS agents. This study provides a crucial step in the translation
of intelligent Raman systems in intraoperative oncological spaces.
