  Needle positioning is essential for various medical applications such as
epidural anaesthesia. Physicians rely on their instincts while navigating the
needle in epidural spaces. Thereby, identifying the tissue structures may be
helpful to the physician as they can provide additional feedback in the needle
insertion process. To this end, we propose a deep neural network that
classifies the tissues from the phase and intensity data of complex OCT signals
acquired at the needle tip. We investigate the performance of the deep neural
network in a limited labelled dataset scenario and propose a novel contrastive
pretraining strategy that learns invariant representation for phase and
intensity data. We show that with 10% of the training set, our proposed
pretraining strategy helps the model achieve an F1 score of 0.84 whereas the
model achieves an F1 score of 0.60 without it. Further, we analyse the
importance of phase and intensity individually towards tissue classification.

Image and Video Processing;Computer Vision and Pattern Recognition