Activating Associative Disease-Aware Vision Token Memory for LLM-Based X-ray Report Generation

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The model generates higher-quality X-ray reports by associating visual information with disease tokens.
State-of-the-art performance was achieved on benchmark datasets like IU X-ray and MIMIC-CXR.
The method integrates a classification model and visual Hopfield network to improve disease awareness.
It mimics the reporting process of professional doctors, enhancing language model capabilities.

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This approach shows improved report generation in X-ray imaging, utilizing associative memory and visual information.

Wang et al. (2025) studied this question.

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