FlexR: Few-shot Classification with Language Embeddings for Structured Reporting of Chest X-rays
This addresses the challenge of generating consistent and evaluable reports in radiology, though it is incremental as it builds on existing contrastive language-image models.
The paper tackled the problem of automating chest X-ray reporting by predicting clinical findings for structured reporting templates, achieving the ability to assess cardiomegaly severity and localize pathologies with limited training annotations.
The automation of chest X-ray reporting has garnered significant interest due to the time-consuming nature of the task. However, the clinical accuracy of free-text reports has proven challenging to quantify using natural language processing metrics, given the complexity of medical information, the variety of writing styles, and the potential for typos and inconsistencies. Structured reporting and standardized reports, on the other hand, can provide consistency and formalize the evaluation of clinical correctness. However, high-quality annotations for structured reporting are scarce. Therefore, we propose a method to predict clinical findings defined by sentences in structured reporting templates, which can be used to fill such templates. The approach involves training a contrastive language-image model using chest X-rays and related free-text radiological reports, then creating textual prompts for each structured finding and optimizing a classifier to predict clinical findings in the medical image. Results show that even with limited image-level annotations for training, the method can accomplish the structured reporting tasks of severity assessment of cardiomegaly and localizing pathologies in chest X-rays.