Contrastive Attention for Automatic Chest X-ray Report Generation (2021.findings-acl)
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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. |
| Outcome: | The proposed model can be easily incorporated into existing models to boost their performance under most metrics. |
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| Challenge: | Existing methods for medical report generation are unable to capture useful information from historical cases. |
| Approach: | They propose a model that captures both visual and semantic information from similar cases. |
| Outcome: | The proposed model outperforms the state-of-the-art models on almost all metrics on IU X-Ray and MIMIC-CXR benchmarks. |
Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation (2021.findings-emnlp)
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| Challenge: | Radiology report generation aims at generating descriptive text from radiology images automatically. |
| Approach: | They propose a weakly supervised contrastive loss method that generates descriptive text from radiology images automatically. |
| Outcome: | The proposed method outperforms previous work on correctness and text generation metrics for two public benchmarks. |
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. |
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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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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. |
| Approach: | They propose to integrate patient data from ED records into multimodal language models that embed patient data into a language model. |
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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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Dynamic Knowledge Prompt for Chest X-ray Report Generation (2024.lrec-main)
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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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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. |
| Approach: | They propose a method to align, concatenate and fuse the current and prior visual information into a joint longitudinal representation which can be provided to a multimodal report generation model. |
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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. |
Coherent and Concise Radiology Report Generation via Context Specific Image Representations and Orthogonal Sentence States (2021.naacl-industry)
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| Challenge: | Neural models for text generation are often designed in an end-to-end fashion, limiting their practical usability in downstream applications. |
| Approach: | They propose a method to compute image representations specific to each sentential context and exploiting diverse sentence states to ensure topical continuity and content diversity of generated radiology reports. |
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