Model Reduction of Shallow CNN Model for Reliable Deployment of Information Extraction from Medical Reports

TL;DR

This paper introduces a model reduction technique for shallow CNNs used in extracting information from medical reports, enhancing interpretability without sacrificing accuracy by linking CNN filters to relevant text segments.

Contribution

It develops a model reduction method that makes shallow CNNs more interpretable by approximating them with sparse, non-negative linear transformations of n-gram features.

Findings

Reduced model complexity while maintaining accuracy

Improved interpretability of CNN filter relevance

Bridged the gap between accuracy and explainability

Abstract

Shallow Convolution Neural Network (CNN) is a time-tested tool for the information extraction from cancer pathology reports. Shallow CNN performs competitively on this task to other deep learning models including BERT, which holds the state-of-the-art for many NLP tasks. The main insight behind this eccentric phenomenon is that the information extraction from cancer pathology reports require only a small number of domain-specific text segments to perform the task, thus making the most of the texts and contexts excessive for the task. Shallow CNN model is well-suited to identify these key short text segments from the labeled training set; however, the identified text segments remain obscure to humans. In this study, we fill this gap by developing a model reduction tool to make a reliable connection between CNN filters and relevant text segments by discarding the spurious connections. We reduce the complexity of shallow CNN representation by approximating it with a linear transformation of n-gram presence representation with a non-negativity and sparsity prior on the transformation weights to obtain an interpretable model. Our approach bridge the gap between the conventionally perceived trade-off boundary between accuracy on the one side and explainability on the other by model reduction.