  Deep learning models have demonstrated remarkable results for various
computer vision tasks, including the realm of medical imaging. However, their
application in the medical domain is limited due to the requirement for large
amounts of training data, which can be both challenging and expensive to
obtain. To mitigate this, pre-trained models have been fine-tuned on
domain-specific data, but such an approach can suffer from inductive biases.
Furthermore, deep learning models struggle to learn the relationship between
spatially distant features and their importance, as convolution operations
treat all pixels equally. Pioneering a novel solution to this challenge, we
employ the Image Foresting Transform to optimally segment images into
superpixels. These superpixels are subsequently transformed into
graph-structured data, enabling the proficient extraction of features and
modeling of relationships using Graph Neural Networks (GNNs). Our method
harnesses an ensemble of three distinct GNN architectures to boost its
robustness. In our evaluations targeting pneumonia classification, our
methodology surpassed prevailing Deep Neural Networks (DNNs) in performance,
all while drastically cutting down on the parameter count. This not only trims
down the expenses tied to data but also accelerates training and minimizes
bias. Consequently, our proposition offers a sturdy, economically viable, and
scalable strategy for medical image classification, significantly diminishing
dependency on extensive training data sets.
