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EMNLP Findings2025

The More, The Better? A Critical Study of Multimodal Context in Radiology Report Summarization

Mong Yuan Sim, Wei Emma Zhang, Xiang Dai, Biaoyan Fang, Sarbin Ranjitkar, Arjun Burlakoti, Jamie Taylor, Haojie Zhuang

摘要

The IMPRESSION section of a radiology report summarizes critical findings of a radiology report and thus plays a crucial role in communication between radiologists and physicians. Research on radiology report summarization mostly focuses on generating the IMPRESSION section by summarizing information from the FINDINGS section, which typically details the radiologist's observations in the radiology images. Recent work start to explore how to incorporate radiology images as input to multimodal summarization models, with the assumption that it can improve generated summary quality, as it contains richer information. However, the real effectiveness of radiology images remains inconclusive. To address this, we conduct a thorough analysis to understand whether existing multimodal models can effectively utilize radiology images in generating a summary of the FINDINGS section. Our analysis reveals that multimodal models might not actually make use of radiology images. For example, masking the image inputs leads to minimal or no performance drop compared to the original images when they are used as input to a trained multimodal summarizer. An expert annotation study on two widely used datasets also shows that radiology images are often unnecessary for writing the IMPRESSION section if FINDINGS section is provided 1 .

No evidence of pneumonia. LM

models to u9lize radiology images

There is heart enlargement.

No plural effusion.

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它引用的 Paper11

  1. Learning Transferable Visual Models From Natural Language SupervisionAlec Radford, Jong Wook Kim, Chris Hallacy, Aditya Ramesh 等ICML 2021 · 47,906 次引用
  2. An Image is Worth 16x16 Words: Transformers for Image Recognition at ScaleAlexey Dosovitskiy, Lucas Beyer, Alexander Kolesnikov, Dirk Weissenborn 等ICLR 2021 · 21,477 次引用
  3. BART: Denoising Sequence-to-Sequence Pre-training for Natural Language Generation, Translation, and ComprehensionMike Lewis, Yinhan Liu, Naman Goyal, Marjan Ghazvininejad 等ACL 2020 · 1,224 次引用
  4. Combining Automatic Labelers and Expert Annotations for Accurate Radiology Report Labeling Using BERTAkshay Smit, Saahil Jain, Pranav Rajpurkar, Anuj Pareek 等EMNLP 2020 · 212 次引用
  5. Vision Guided Generative Pre-trained Language Models for Multimodal Abstractive SummarizationTiezheng Yu, Wenliang Dai, Zihan Liu, Pascale FungEMNLP 2021 · 64 次引用

相关 Paper

  1. Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report SummarizationSanjeev Kumar Karn, Ning Liu, Hinrich Schütze, Oladimeji FarriACL 2022
  2. Improving Radiology Summarization with Radiograph and Anatomy PromptsJinpeng Hu, Zhihong Chen, Yang Liu, Xiang Wan 等ACL Findings 2023 · 11 次引用
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  4. Graph Enhanced Contrastive Learning for Radiology Findings SummarizationJinpeng Hu, Zhuo Li, Zhihong Chen, Zhen Li 等ACL 2022 · 40 次引用
  5. Multimodal Dual-Path Decoding for Medical Report GenerationJinghan Sun, Dong Wei, Zhihong Zhu, Yuyang Xue 等ACL Findings 2026
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