DDGIP: Radiology Report Generation Through Disease Description Graph and Informed Prompting (2025.findings-naacl)
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| Challenge: | Automatic radiology report generation is challenging due to inherent biases in medical imaging data. |
| Approach: | They propose a disease description graph that encapsulates comprehensive and pertinent disease information. |
| Outcome: | The proposed model outperforms state-of-the-art models on two widely-used datasets . the proposed model is based on a three-layer decoder and improves on existing models . |
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| Challenge: | Existing methods for radiology report generation fail to incorporate prior knowledge . data bias, sparse features of chest X-ray image make it difficult to generate reports . |
| Approach: | They propose a dynamically integrated framework for chest X-ray report generation that incorporates pulmonary lesion knowledge at the instance-level. |
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Style-Aware Radiology Report Generation with RadGraph and Few-Shot Prompting (2023.findings-emnlp)
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Benjamin Yan, Ruochen Liu, David Kuo, Subathra Adithan, Eduardo Reis, Stephen Kwak, Vasantha Venugopal, Chloe O’Connell, Agustina Saenz, Pranav Rajpurkar, Michael Moor
| Challenge: | Existing methods for generating reports from medical images conflate the content of the report with its style, which can lead to inaccurate reports. |
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ORGAN: Observation-Guided Radiology Report Generation via Tree Reasoning (2023.acl-long)
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| Challenge: | Existing methods to generate radiology reports only rely on high-level plans, but they lack important information. |
| Approach: | They propose an Observation-guided radiology Report Generation framework which generates free-text descriptions for a set of radiographs. |
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RECAP: Towards Precise Radiology Report Generation via Dynamic Disease Progression Reasoning (2023.findings-emnlp)
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| Challenge: | Recent studies have focused on producing concise observations while neglecting the precise attributes that determine the severity of diseases. |
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KIA: Knowledge-Guided Implicit Vision-Language Alignment for Chest X-Ray Report Generation (2025.coling-main)
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| Challenge: | Existing reports on medical images and reports lack fine-grained cross-modal interaction, leading to insufficient understanding of detailed information. |
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Replace and Report: NLP Assisted Radiology Report Generation (2023.findings-acl)
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| Challenge: | Clinical practice frequently uses medical imaging for diagnosis and treatment. |
| Approach: | They propose a template-based approach to generate radiology reports from radiographs . they use multilabel image classifiers to generate tags, pathological descriptions from tags . |
| Outcome: | The proposed method improves on the most popular radiology report datasets. |
Divide and Conquer Radiology Report Generation via Observation Level Fine-grained Pretraining and Prompt Tuning (2024.emnlp-main)
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| Challenge: | Recent advances in image captioning and vision-language pretraining have made it difficult for radiologists to generate coherent and accurate reports. |
| Approach: | They propose a model which breaks down full-text radiology reports into concise observation descriptions and encodes observation predictions into a decoding stage. |
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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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| Challenge: | Existing encoder-decoder models suffer from hallucinations, generating plausible but incorrect medical findings. |
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Rad-Flamingo: A Multimodal Prompt driven Radiology Report Generation Framework with Patient-Centric Explanations (2026.findings-eacl)
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| Challenge: | Existing reports are labor-intensive and expert-intensive, resulting in inconsistencies and a lack of patient-centered insight. |
| Approach: | They propose a multimodal prompt-driven report generation framework that integrates diverse data modalities to produce comprehensive and context-aware radiology reports. |
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