Explaining Chest X-ray Pathologies in Natural Language

TL;DR

This paper introduces a new task and dataset for generating natural language explanations for chest X-ray diagnoses, aiming to improve model interpretability in medical imaging.

Contribution

It presents MIMIC-NLE, the first large-scale dataset with natural language explanations for chest X-ray pathologies, and evaluates models to generate these explanations.

Findings

Models can generate human-like explanations for X-ray diagnoses.

Clinician assessments show the explanations are informative and reliable.

The dataset enables training intrinsically explainable models.

Abstract

Most deep learning algorithms lack explanations for their predictions, which limits their deployment in clinical practice. Approaches to improve explainability, especially in medical imaging, have often been shown to convey limited information, be overly reassuring, or lack robustness. In this work, we introduce the task of generating natural language explanations (NLEs) to justify predictions made on medical images. NLEs are human-friendly and comprehensive, and enable the training of intrinsically explainable models. To this goal, we introduce MIMIC-NLE, the first, large-scale, medical imaging dataset with NLEs. It contains over 38,000 NLEs, which explain the presence of various thoracic pathologies and chest X-ray findings. We propose a general approach to solve the task and evaluate several architectures on this dataset, including via clinician assessment.