Image-aware Evaluation of Generated Medical Reports

• URL: http://arxiv.org/abs/2410.17357v1
• Date: Tue, 22 Oct 2024 18:50:20 GMT
• Title: Image-aware Evaluation of Generated Medical Reports
• Authors: Gefen Dawidowicz, Elad Hirsch, Ayellet Tal,
• Abstract summary: The paper proposes a novel evaluation metric for automatic medical report generation from X-ray images, VLScore. The key idea of our metric is to measure the similarity between radiology reports while considering the corresponding image. We demonstrate the benefit of our metric through evaluation on a dataset where radiologists marked errors in pairs of reports, showing notable alignment with radiologists' judgments.
• Score: 11.190146577567548
• License:
• Abstract: The paper proposes a novel evaluation metric for automatic medical report generation from X-ray images, VLScore. It aims to overcome the limitations of existing evaluation methods, which either focus solely on textual similarities, ignoring clinical aspects, or concentrate only on a single clinical aspect, the pathology, neglecting all other factors. The key idea of our metric is to measure the similarity between radiology reports while considering the corresponding image. We demonstrate the benefit of our metric through evaluation on a dataset where radiologists marked errors in pairs of reports, showing notable alignment with radiologists' judgments. In addition, we provide a new dataset for evaluating metrics. This dataset includes well-designed perturbations that distinguish between significant modifications (e.g., removal of a diagnosis) and insignificant ones. It highlights the weaknesses in current evaluation metrics and provides a clear framework for analysis.

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