Improving Factual Completeness and Consistency of Image-to-Text Radiology Report Generation

Neural image-to-text radiology report generation systems offer the potential to accelerate clinical processes by saving radiologists from the repetitive labor of drafting radiology reports and preventing medical errors. However, existing report generation systems, despite achieving high performances on natural language generation metrics such as CIDEr or BLEU, still suffer from incomplete and inconsistent generations, rendering these systems unusable in practice. In this work, we aim to overcome this problem by proposing two new metrics that encourage the factual completeness and consistency of generated radiology reports. The first metric, the Exact Entity Match score, evaluates a generation by its coverage of radiology domain entities against the references. The second metric, the Entailing Entity Match score, augments the first metric by introducing a natural language inference model into the entity match process to encourage consistent generations that can be entailed from the references. To achieve this, we also developed an in-domain NLI model via weak supervision to improve its performance on radiology text. We further propose a report generation system that optimizes these two new metrics via reinforcement learning. On two open radiology report datasets, our system not only achieves the best performance on these two metrics compared to baselines, but also leads to as much as +2.0 improvement on the F1 score of a clinical finding metric. We show via analysis and examples that our system leads to generations that are more complete and consistent compared to the baselines.

Contrastive Learning of Medical Visual Representations from Paired Images and Text

Learning visual representations of medical images is core to medical image understanding but its progress has been held back by the small size of hand-labeled datasets. Existing work commonly relies on transferring weights from ImageNet pretraining, which is suboptimal due to drastically different image characteristics, or rule-based label extraction from the textual report data paired with medical images, which is inaccurate and hard to generalize. We propose an alternative unsupervised strategy to learn medical visual representations directly from the naturally occurring pairing of images and textual data. Our method of pretraining medical image encoders with the paired text data via a bidirectional contrastive objective between the two modalities is domain-agnostic, and requires no additional expert input. We test our method by transferring our pretrained weights to 4 medical image classification tasks and 2 zero-shot retrieval tasks, and show that our method leads to image representations that considerably outperform strong baselines in most settings. Notably, in all 4 classification tasks, our method requires only 10% as much labeled training data as an ImageNet initialized counterpart to achieve better or comparable performance, demonstrating superior data efficiency.