Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays

Understand

Automatic medical image report generation has drawn growing attention due to its potential to alleviate radiologists' workload.

• Existing work on report generation often trains encoder-decoder networks to generate complete reports.
• However, such models are affected by data bias (e.g.~label imbalance) and face common issues inherent in text generation models (e.g.~repetition).
• In this work, we focus on reporting abnormal findings on radiology images; instead of training on complete radiology reports, we propose a method to identify abnormal findings from the reports in addition to grouping them with unsupervised clustering and minimal rules.