Radiology Report Generation

What is it about?

In this paper, we introduce a radiology report generation network termed Dynamics Priori Networks (DPN), which leverages a dynamic knowledge graph and prior knowledge. Concretely, we establish an adaptable graph network and harness both medical domain knowledge and expert insights to enhance the model’s intelligence. Notably, we introduce an image-text contrastive module and an image-text matching module to enhance the quality of the generated results.

Featured Image

Photo by CDC on Unsplash

Photo by CDC on Unsplash

Why is it important?

Radiology images are frequently used in clinical practice and medical trials for the diagnosis and treatment of various medical conditions[1]. Writing radiology reports can indeed be a time-consuming and error-prone task, especially for inexperienced radiologists. This has led to a growing demand for automated radiology report generation systems.

Perspectives

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I hope this article can draw more attention to how much help the development of medical artificial intelligence can bring to remote areas.

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