Shadow and Light: Digitally Reconstructed Radiographs for Disease Classification

TL;DR

This paper introduces DRR-RATE, a large synthetic chest X-ray dataset from CT scans, enabling advanced multimodal disease classification research with high-quality, controllable images and labels, validated by experiments with existing models.

Contribution

The creation of DRR-RATE, a comprehensive synthetic X-ray dataset from CT scans, facilitating multimodal research and improving disease classification methods.

Findings

CheXnet trained on DRR-RATE achieves high AUC scores for key pathologies.

CheXnet trained on CheXpert generalizes well to DRR-RATE images.

DRR images effectively capture critical pathology features from CT data.

Abstract

In this paper, we introduce DRR-RATE, a large-scale synthetic chest X-ray dataset derived from the recently released CT-RATE dataset. DRR-RATE comprises of 50,188 frontal Digitally Reconstructed Radiographs (DRRs) from 21,304 unique patients. Each image is paired with a corresponding radiology text report and binary labels for 18 pathology classes. Given the controllable nature of DRR generation, it facilitates the inclusion of lateral view images and images from any desired viewing position. This opens up avenues for research into new and novel multimodal applications involving paired CT, X-ray images from various views, text, and binary labels. We demonstrate the applicability of DRR-RATE alongside existing large-scale chest X-ray resources, notably the CheXpert dataset and CheXnet model. Experiments demonstrate that CheXnet, when trained and tested on the DRR-RATE dataset, achieves sufficient to high AUC scores for the six common pathologies cited in common literature: Atelectasis, Cardiomegaly, Consolidation, Lung Lesion, Lung Opacity, and Pleural Effusion. Additionally, CheXnet trained on the CheXpert dataset can accurately identify several pathologies, even when operating out of distribution. This confirms that the generated DRR images effectively capture the essential pathology features from CT images. The dataset and labels are publicly accessible at https://huggingface.co/datasets/farrell236/DRR-RATE.