  The increasing prevalence of prostate cancer has led to the widespread
adoption of Robotic-Assisted Surgery (RAS) as a treatment option. Sentinel
lymph node biopsy (SLNB) is a crucial component of prostate cancer surgery and
requires accurate diagnostic evidence. This procedure can be improved by using
a drop-in gamma probe, SENSEI system, to distinguish cancerous tissue from
normal tissue. However, manual control of the probe using live gamma level
display and audible feedback could be challenging for inexperienced surgeons,
leading to the potential for missed detections. In this study, a deep imitation
training workflow was proposed to automate the radioactive node detection
procedure. The proposed training workflow uses simulation data to train an
end-to-end vision-based gamma probe manipulation agent. The evaluation results
showed that the proposed approach was capable to predict the next-step action
and holds promise for further improvement and extension to a hardware setup.
