Improving medical report generation
Problem being addressed
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.
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. NSL conducts human-like symbolic logical reasoning that realizes unsupervised causal effect analysis of detected entities of abnormalities through meta-interpretive learning. NSL fills these discoveries of target diseases into a unified template, successfully achieving a comprehensive medical report generation.
Advantages of this solution
When 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.
Solution originally applied in these industries
Possible New Application of the Work
The suggested solution is a human-like learning framework with visual perception ability and high-level logical reasoning strength. This combination can boost the generalization and interpretability of neural learning, also give a robust solution naturally. It can be an effective and scalable solution to relieve laborious workloads.
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