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Automatic medical image report generation has drawn growing attention due toits potential to alleviate radiologists' workload. Existing work on reportgeneration often trains encoder-decoder networks to generate complete reports.However, such models are affected by data bias (e.g.~label imbalance) and facecommon issues inherent in text generation models (e.g.~repetition). In thiswork, we focus on reporting abnormal findings on radiology images; instead oftraining on complete radiology reports, we propose a method to identifyabnormal findings from the reports in addition to grouping them withunsupervised clustering and minimal rules. We formulate the task as cross-modalretrieval and propose Conditional Visual-Semantic Embeddings to align imagesand fine-grained abnormal findings in a joint embedding space. We demonstratethat our method is able to retrieve abnormal findings and outperforms existinggeneration models on both clinical correctness and text generation metrics.

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