Dia-LLaMA: Towards Large Language Model-driven CT Report Generation

Understand

Medical report generation has achieved remarkable advancements yet has still been faced with several challenges.

• First, the inherent imbalance in the distribution of normal and abnormal cases may lead models to exhibit a biased focus on normal samples, resulting in unreliable diagnoses.
• Second, the frequent occurrence of common template sentences in the reports may overwhelm the critical abnormal information.
• Moreover, existing works focus on 2D chest X-rays, leaving CT report generation underexplored due to the high-dimensional nature of CT images and the limited availability of CT-report pairs.