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The chest X-Ray (CXR) is the one of the most common clinical exam used todiagnose thoracic diseases and abnormalities. The volume of CXR scans generateddaily in hospitals is huge. Therefore, an automated diagnosis system able tosave the effort of doctors is of great value. At present, the applications ofartificial intelligence in CXR diagnosis usually use pattern recognition toclassify the scans. However, such methods rely on labeled databases, which arecostly and usually have large error rates. In this work, we built a databasecontaining more than 12,000 CXR scans and radiological reports, and developed amodel based on deep convolutional neural network and recurrent network withattention mechanism. The model learns features from the CXR scans and theassociated raw radiological reports directly; no additional labeling of thescans are needed. The model provides automated recognition of given scans andgeneration of reports. The quality of the generated reports was evaluated withboth the CIDEr scores and by radiologists as well. The CIDEr scores are foundto be around 5.8 on average for the testing dataset. Further blind evaluationsuggested a comparable performance against human radiologist.

Automated Radiological Report Generation For Chest X-Rays With Weakly-Supervised End-to-End Deep Learning