Neuro-Symbolic Learning: Principles and Applications in Ophthalmology
It addresses interpretability and reasoning problems in critical applications like healthcare, but is incremental as it reviews existing methods rather than proposing new ones.
This review surveys neuro-symbolic learning (NeSyL) approaches that integrate symbolic representations into neural networks to address challenges like interpretability and explainability, highlighting applications in domains such as ophthalmology and health informatics.
Neural networks have been rapidly expanding in recent years, with novel strategies and applications. However, challenges such as interpretability, explainability, robustness, safety, trust, and sensibility remain unsolved in neural network technologies, despite the fact that they will unavoidably be addressed for critical applications. Attempts have been made to overcome the challenges in neural network computing by representing and embedding domain knowledge in terms of symbolic representations. Thus, the neuro-symbolic learning (NeSyL) notion emerged, which incorporates aspects of symbolic representation and bringing common sense into neural networks (NeSyL). In domains where interpretability, reasoning, and explainability are crucial, such as video and image captioning, question-answering and reasoning, health informatics, and genomics, NeSyL has shown promising outcomes. This review presents a comprehensive survey on the state-of-the-art NeSyL approaches, their principles, advances in machine and deep learning algorithms, applications such as opthalmology, and most importantly, future perspectives of this emerging field.