  Automated medical report generation in spine radiology, i.e., given spinal
medical images and directly create radiologist-level diagnosis reports to
support clinical decision making, is a novel yet fundamental study in the
domain of artificial intelligence in healthcare. However, it is incredibly
challenging because it is an extremely complicated task that involves visual
perception and high-level reasoning processes. In this paper, we propose the
neural-symbolic learning (NSL) framework that performs human-like learning by
unifying deep neural learning and symbolic logical reasoning for the spinal
medical report generation. Generally speaking, the NSL framework firstly
employs deep neural learning to imitate human visual perception for detecting
abnormalities of target spinal structures. Concretely, we design an adversarial
graph network that interpolates a symbolic graph reasoning module into a
generative adversarial network through embedding prior domain knowledge,
achieving semantic segmentation of spinal structures with high complexity and
variability. NSL secondly conducts human-like symbolic logical reasoning that
realizes unsupervised causal effect analysis of detected entities of
abnormalities through meta-interpretive learning. NSL finally fills these
discoveries of target diseases into a unified template, successfully achieving
a comprehensive medical report generation. When it employed in a real-world
clinical dataset, a series of empirical studies demonstrate its capacity on
spinal medical report generation as well as show that our algorithm remarkably
exceeds existing methods in the detection of spinal structures. These indicate
its potential as a clinical tool that contributes to computer-aided diagnosis.
