  With the increasing adoption of machine learning tools like neural networks
across several domains, interesting connections and comparisons to concepts
from other domains are coming to light. In this work, we focus on the class of
Tensor Networks, which has been a work horse for physicists in the last two
decades to analyse quantum many-body systems. Building on the recent interest
in tensor networks for machine learning, we extend the Matrix Product State
tensor networks (which can be interpreted as linear classifiers operating in
exponentially high dimensional spaces) to be useful in medical image analysis
tasks. We focus on classification problems as a first step where we motivate
the use of tensor networks and propose adaptions for 2D images using classical
image domain concepts such as local orderlessness of images. With the proposed
locally orderless tensor network model (LoTeNet), we show that tensor networks
are capable of attaining performance that is comparable to state-of-the-art
deep learning methods. We evaluate the model on two publicly available medical
imaging datasets and show performance improvements with fewer model
hyperparameters and lesser computational resources compared to relevant
baseline methods.
