# MCA-RG: Enhancing LLMs with Medical Concept Alignment for Radiology Report Generation

arXiv: https://arxiv.org/abs/2507.06992 · HTML: https://mlsanity.com/paper/2507.06992/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 21 of its 32 references that are in the map.
- First posted on arXiv 9 Jul 2025.

## Builds on (most cited first, 21 of 21)

- [Llama 2: Open Foundation and Fine-Tuned Chat Models](https://mlsanity.com/paper/2307.09288/) (2023)
- [End-to-End Object Detection with Transformers](https://mlsanity.com/paper/2005.12872/) (2020)
- [Publicly Available Clinical BERT Embeddings](https://mlsanity.com/paper/1904.03323/) (2019)
- [MedCLIP: Contrastive Learning from Unpaired Medical Images and Text](https://mlsanity.com/paper/2210.10163/) (2022)
- [Generating Radiology Reports via Memory-driven Transformer](https://mlsanity.com/paper/2010.16056/) (2020)
- [RadGraph: Extracting Clinical Entities and Relations from Radiology Reports](https://mlsanity.com/paper/2106.14463/) (2021)
- [Cross-modal Memory Networks for Radiology Report Generation](https://mlsanity.com/paper/2204.13258/) (2022)
- [Interactive and Explainable Region-guided Radiology Report Generation](https://mlsanity.com/paper/2304.08295/) (2023)
- [CheXpert Plus: Augmenting a Large Chest X-ray Dataset with Text Radiology Reports, Patient Demographics and Additional Image Formats](https://mlsanity.com/paper/2405.19538/) (2024)
- [Improving Chest X-Ray Report Generation by Leveraging Warm Starting](https://mlsanity.com/paper/2201.09405/) (2022)
- [Chest ImaGenome Dataset for Clinical Reasoning](https://mlsanity.com/paper/2108.00316/) (2021)
- [Contrastive Attention for Automatic Chest X-ray Report Generation](https://mlsanity.com/paper/2106.06965/) (2021)
- [KiUT: Knowledge-injected U-Transformer for Radiology Report Generation](https://mlsanity.com/paper/2306.11345/) (2023)
- [Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-Ray Reports](https://mlsanity.com/paper/2004.12274/) (2020)
- [Progressive Transformer-Based Generation of Radiology Reports](https://mlsanity.com/paper/2102.09777/) (2021)
- [HERGen: Elevating Radiology Report Generation with Longitudinal Data](https://mlsanity.com/paper/2407.15158/) (2024)
- [MiniGPT-Med: Large Language Model as a General Interface for Radiology Diagnosis](https://mlsanity.com/paper/2407.04106/) (2024)
- [Fine-Grained Image-Text Alignment in Medical Imaging Enables Explainable Cyclic Image-Report Generation](https://mlsanity.com/paper/2312.08078/) (2023)
- [Anatomy-specific classification of medical images using deep convolutional nets](https://mlsanity.com/paper/1504.04003/) (2015)
- [Mitigating Bias: Enhancing Image Classification by Improving Model Explanations](https://mlsanity.com/paper/2307.01473/) (2023)
- [ORID: Organ-Regional Information Driven Framework for Radiology Report Generation](https://mlsanity.com/paper/2411.13025/) (2024)

## Built on by (most cited first, 3 of 3)

- [R2GenKG: Hierarchical Multi-modal Knowledge Graph for LLM-based Radiology Report Generation](https://mlsanity.com/paper/2508.03426/) (2025)
- [OraPO: Oracle-educated Reinforcement Learning for Data-efficient and Factual Radiology Report Generation](https://mlsanity.com/paper/2509.18600/) (2025)
- [MAC-RRG: Iterative Multi-Agent Collaboration for X-ray Radiology Report Generation](https://mlsanity.com/paper/2609.26124/) (2026)

## Abstract (arXiv)

Despite significant advancements in adapting Large Language Models (LLMs) for radiology report generation (RRG), clinical adoption remains challenging due to difficulties in accurately mapping pathological and anatomical features to their corresponding text descriptions. Additionally, semantic agnostic feature extraction further hampers the generation of accurate diagnostic reports. To address these challenges, we introduce Medical Concept Aligned Radiology Report Generation (MCA-RG), a knowledge-driven framework that explicitly aligns visual features with distinct medical concepts to enhance the report generation process. MCA-RG utilizes two curated concept banks: a pathology bank containing lesion-related knowledge, and an anatomy bank with anatomical descriptions. The visual features are aligned with these medical concepts and undergo tailored enhancement. We further propose an anatomy-based contrastive learning procedure to improve the generalization of anatomical features, coupled with a matching loss for pathological features to prioritize clinically relevant regions. Additionally, a feature gating mechanism is employed to filter out low-quality concept features. Finally, the visual features are corresponding to individual medical concepts, and are leveraged to guide the report generation process. Experiments on two public benchmarks (MIMIC-CXR and CheXpert Plus) demonstrate that MCA-RG achieves superior performance, highlighting its effectiveness in radiology report generation.