Normal-Abnormal Decoupling Memory for Medical Report Generation (2023.findings-emnlp)
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| Challenge: | Existing methods for capturing nuanced visual information are prone to data bias and noise. |
| Approach: | They propose a normal-abnormal semantic decoupling network that utilizes abnormal pattern memory to optimize visual extraction through the extraction of abnormal semantics from the reports. |
| Outcome: | The proposed approach surpasses the current state-of-the-art methods on the benchmark MIMIC-CXR and shows excellent performance on the same dataset. |
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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. |
| Outcome: | The proposed method outperforms existing generation models on correctness and text generation metrics. |
Writing by Memorizing: Hierarchical Retrieval-based Medical Report Generation (2021.acl-long)
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| Challenge: | Existing methods for medical image analysis use predefined template databases or ignore hierarchical nature of medical report generation. |
| Approach: | They propose a hierarchical retrieval mechanism to extract both report and sentence-level templates for clinically accurate report generation. |
| Outcome: | The proposed model extracts both report and sentence-level templates for clinically accurate report generation. |
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. |
Cross-modal Contrastive Attention Model for Medical Report Generation (2022.coling-1)
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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. |
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 . |
| Outcome: | The proposed method achieves fluency and clinical accuracy on common metrics. |
Generating Radiology Reports via Memory-driven Transformer (2020.emnlp-main)
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| Challenge: | Medical imaging reports are time-consuming and can be error-prone for inexperienced radiologists. |
| Approach: | They propose to generate radiology reports with memory-driven Transformer using relational memory and memory-based conditional layer normalization. |
| Outcome: | The proposed method outperforms existing models on IU X-Ray and MIMIC-CXR . it generates long reports with medical terms and meaningful image-text attention mappings . |
Fine-grained Medical Vision-Language Representation Learning for Radiology Report Generation (2023.emnlp-main)
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| Challenge: | Existing methods to learn medical vision-language representations by contrasting images with entire reports are not effective. |
| Approach: | They propose a phenotype-driven medical vision-language representation learning framework to bridge the gap between visual and textual modalities for improved text-oriented generation. |
| Outcome: | The proposed framework bridges the gap between visual and textual modalities for improved radiology report generation. |
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. |
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. |
| Approach: | They propose a multi-task learning framework which predicts tags and generates paragraphs for abnormal regions in medical images. |
| Outcome: | The proposed framework can generate long paragraphs on two publicly available datasets. |
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. |
| Outcome: | The proposed model incorporates patient data from the MIMIC-CXR and MIMICIV-ED datasets to improve diagnostic accuracy and improves radiologist effectiveness. |