Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports (P19-1)
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| Challenge: | Existing studies do not consider the complex structure information between and within report sections. |
| Approach: | They propose a framework which exploits the structure information between and within report sections for generating CXR imaging reports. |
| Outcome: | The proposed framework achieves state-of-the-art performance on two CXR report datasets. |
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| Challenge: | Recent studies show that learning-based models fail to accurately capture and describe abnormal regions due to data bias. |
| Approach: | They propose a model that compares the current input image with normal images to capture abnormal regions by contrasting the input image and normal images. |
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The Impact of Auxiliary Patient Data on Automated Chest X-Ray Report Generation and How to Incorporate It (2025.acl-long)
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| Challenge: | Traditionally, CXR report generation relies on data from a patient’s exam, overlooking valuable information from patient electronic health records. |
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Multimodal Generation of Radiology Reports using Knowledge-Grounded Extraction of Entities and Relations (2022.aacl-main)
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Francesco Dalla Serra, William Clackett, Hamish MacKinnon, Chaoyang Wang, Fani Deligianni, Jeff Dalton, Alison Q. O’Neil
| Challenge: | Existing approaches to generate text radiology reports are prone to errors and poor clinical accuracy. |
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Controllable Chest X-Ray Report Generation from Longitudinal Representations (2023.findings-emnlp)
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| Challenge: | Radiology reports are detailed text descriptions of the content of medical scans. |
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Automated Generation of Accurate & Fluent Medical X-ray Reports (2021.emnlp-main)
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| Challenge: | Existing medical report generation efforts focus on producing human-readable reports, yet the generated text may not be well aligned to the clinical facts. |
| Approach: | They propose to automate the generation of medical reports from chest X-ray image inputs . medical reports are the primary medium, which physicians communicate findings from scans - authors say . |
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Differentiable Multi-Agent Actor-Critic for Multi-Step Radiology Report Summarization (2022.acl-long)
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| Challenge: | Prior research on radiology report summarization has focused on single-step end-to-end models which subsume the task of salient content acquisition. |
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On the Automatic Generation of Medical Imaging Reports (P18-1)
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| Challenge: | a complete medical imaging report contains multiple heterogeneous forms of information, including findings and tags . abnormal regions in medical images are difficult to identify and the reports are typically long, containing multiple sentences. |
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Automated Structured Radiology Report Generation (2025.acl-long)
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Jean-Benoit Delbrouck, Justin Xu, Johannes Moll, Alois Thomas, Zhihong Chen, Sophie Ostmeier, Asfandyar Azhar, Kelvin Zhenghao Li, Andrew Johnston, Christian Bluethgen, Eduardo Pontes Reis, Mohamed S Muneer, Maya Varma, Curtis Langlotz
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Learning Visual-Semantic Embeddings for Reporting Abnormal Findings on Chest X-rays (2020.findings-emnlp)
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| Challenge: | Existing work on report generation often trains encoder-decoder networks to generate complete reports, but such models are affected by data bias and face common issues inherent in text generation models. |
| Approach: | They propose a method to identify abnormal findings from radiology images and group them with unsupervised clustering and minimal rules. |
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Toward Expanding the Scope of Radiology Report Summarization to Multiple Anatomies and Modalities (2023.acl-short)
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| Challenge: | Existing studies are limited to a single modality and a chest X-ray, making it difficult to replicate results or compare approaches. |
| Approach: | They propose a dataset to generate an impression section of a radiology report . they propose to use three new modalities and seven new anatomies to evaluate their models . |
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