MonteRET: AI Agent Enhancing Multimodal LLMs with Multi-granularity Knowledge Retrieval for Chest CT Report Generation
MonteRET is a region-aware retrieval-enhanced framework for generating chest CT findings sections. It integrates global and regional CT features, retrieves clinically relevant knowledge, and refines reports via a knowledge-guided rewriting agent. Evaluated on public and external cohorts, MonteRET improved report quality, semantic similarity, and clinical efficacy, with experts favoring its outputs.
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[Submitted on 15 Jul 2026]
Title:MonteRET: AI Agent Enhancing Multimodal LLMs with Multi-granularity Knowledge Retrieval for Chest CT Report Generation
View a PDF of the paper titled MonteRET: AI Agent Enhancing Multimodal LLMs with Multi-granularity Knowledge Retrieval for Chest CT Report Generation, by Yi Lin and 10 other authors
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Abstract:Automated chest CT report generation remains challenging because clinically faithful reporting requires both whole-volume understanding and accurate description of localized anatomical findings. Here we developed and retrospectively evaluated MonteRET, a region-aware retrieval-enhanced framework for generating chest CT findings sections. MonteRET integrates global CT features with region-level anatomical representations, retrieves clinically relevant knowledge using predicted medical conditions and region-level vision-language alignment, and refines initial reports through a knowledge-guided report rewriting agent. We trained our model on a public cohort with 24,128 CT scans from RadGenome-ChestCT. We evaluated MonteRET on the public RadGenome-ChestCT test set of 1,564 CT scans and an external cohort of 82 CT scans from NewYork-Presbyterian/Weill Cornell Medical Center. MonteRET improved report quality, semantic similarity, and clinical efficacy compared with a matched baseline and several state-of-the-art methods. Gains were most pronounced for recall, suggesting fewer omitted findings. Human expert evaluation by radiology residents also favored MonteRET.
Subjects:
Computer Vision and Pattern Recognition (cs.CV); Computation and Language (cs.CL)
Cite as: arXiv:2607.14264 [cs.CV]
(or arXiv:2607.14264v1 [cs.CV] for this version)
https://doi.org/10.48550/arXiv.2607.14264
arXiv-issued DOI via DataCite (pending registration)
Submission history
From: Yi Lin [view email] [v1] Wed, 15 Jul 2026 18:17:13 UTC (2,124 KB)
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