Symbolic Knowledge Extraction and Distillation into Convolutional Neural Networks to Improve Medical Image Classification

Abstract

Convolutional Neural Networks (CNNs) have achieved outstanding performance in radiology tasks. However, CNNs lack the transparency and explainability necessary to enable their practical clinical adoption. This paper introduces a neural-symbolic approach allowing domain experts to intervene in the training of CNNs. Following extraction and expert validation of meaningful symbolic knowledge from a trained CNN, such knowledge is distilled back into a streamlined CNN. The approach is shown to enhance user control over conventional CNN training by combining interpretable symbolic representations into a simplified CNN, allowing domain experts to control the decision making process. The kernels in a given CNN layer are mapped to symbolic knowledge representations in the form of logic programming rules. Extracted knowledge is evaluated against known radiomics features, allowing doctors to decide based on best practice which kernels to keep or reject. Expert intervention takes place through relevant knowledge distillation back into a more compact CNN. Our results show that a student CNN can learn successfully even from multiple teachers (different knowledge-bases) to replicate the selected relevant kernels and corresponding classification results. The proposed approach delivers a trainable parameter reduction of at least 56.3% while achieving high cosine similarity for kernel replication and a fidelity score of 99.2%. Expert validation highlights the importance of this approach at fostering greater trust in AI-driven medical decision making.

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