IA-GCN: Interpretable Attention based Graph Convolutional Network for Disease prediction

by   Anees Kazi, et al.

Interpretability in Graph Convolutional Networks (GCNs) has been explored to some extent in computer vision in general, yet, in the medical domain, it requires further examination. Moreover, most of the interpretability approaches for GCNs, especially in the medical domain, focus on interpreting the model in a post hoc fashion. In this paper, we propose an interpretable graph learning-based model which 1) interprets the clinical relevance of the input features towards the task, 2) uses the explanation to improve the model performance and, 3) learns a population level latent graph that may be used to interpret the cohort's behavior. In a clinical scenario, such a model can assist the clinical experts in better decision-making for diagnosis and treatment planning. The main novelty lies in the interpretable attention module (IAM), which directly operates on multi-modal features. Our IAM learns the attention for each feature based on the unique interpretability-specific losses. We show the application on two publicly available datasets, Tadpole and UKBB, for three tasks of disease, age, and gender prediction. Our proposed model shows superior performance with respect to compared methods with an increase in an average accuracy of 3.2 2 clinical interpretation of our results.


page 1

page 2

page 3

page 4


InceptionGCN: Receptive Field Aware Graph Convolutional Network for Disease Prediction

Geometric deep learning provides a principled and versatile manner for t...

Visualization for Histopathology Images using Graph Convolutional Neural Networks

With the increase in the use of deep learning for computer-aided diagnos...

Decision Support for Intoxication Prediction Using Graph Convolutional Networks

Every day, poison control centers (PCC) are called for immediate classif...

Interpretable Models Capable of Handling Systematic Missingness in Imbalanced Classes and Heterogeneous Datasets

Application of interpretable machine learning techniques on medical data...

Domain Invariant Model with Graph Convolutional Network for Mammogram Classification

Due to its safety-critical property, the image-based diagnosis is desire...

Transferability of coVariance Neural Networks and Application to Interpretable Brain Age Prediction using Anatomical Features

Graph convolutional networks (GCN) leverage topology-driven graph convol...

Rationalizing Medical Relation Prediction from Corpus-level Statistics

Nowadays, the interpretability of machine learning models is becoming in...

Please sign up or login with your details

Forgot password? Click here to reset