Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning
This addresses the problem of reducing doctor workload in hospitals by automating report generation for chest X-rays, though it is incremental as it builds on existing deep learning and attention mechanisms.
The authors tackled automated radiological report generation for chest X-rays by developing a weakly-supervised end-to-end deep learning model that learns from scans and raw reports without additional labeling, achieving CIDEr scores around 5.8 and comparable performance to human radiologists in evaluations.
The chest X-Ray (CXR) is the one of the most common clinical exam used to diagnose thoracic diseases and abnormalities. The volume of CXR scans generated daily in hospitals is huge. Therefore, an automated diagnosis system able to save the effort of doctors is of great value. At present, the applications of artificial intelligence in CXR diagnosis usually use pattern recognition to classify the scans. However, such methods rely on labeled databases, which are costly and usually have large error rates. In this work, we built a database containing more than 12,000 CXR scans and radiological reports, and developed a model based on deep convolutional neural network and recurrent network with attention mechanism. The model learns features from the CXR scans and the associated raw radiological reports directly; no additional labeling of the scans are needed. The model provides automated recognition of given scans and generation of reports. The quality of the generated reports was evaluated with both the CIDEr scores and by radiologists as well. The CIDEr scores are found to be around 5.8 on average for the testing dataset. Further blind evaluation suggested a comparable performance against human radiologist.