Papers with Medical imaging
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 . |
A Novel Corpus of Annotated Medical Imaging Reports and Information Extraction Results Using BERT-based Language Models (2024.lrec-main)
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Namu Park, Kevin Lybarger, Giridhar Kaushik Ramachandran, Spencer Lewis, Aashka Damani, Özlem Uzuner, Martin Gunn, Meliha Yetisgen
| Challenge: | Medical imaging reports document radiologists' interpretation of medical images through detailed narrative text. |
| Approach: | They propose a corpus of annotated medical imaging reports (CAMIR) that includes 609 annotation radiology reports from three imaging modality types. |
| Outcome: | The proposed schema captures clinical indications, lesions, and medical problems and can be used in secondary applications. |
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. |
Cross-modal Memory Networks for Radiology Report Generation (2021.acl-long)
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| Challenge: | Medical imaging reports are essential in clinical practice, and generating the reports is beneficial to reduce the burden of radiologists. |
| Approach: | They propose to use a shared memory to enhance the encoder-decoder framework for radiology report generation. |
| Outcome: | The proposed model can generate more accurate reports on two widely used datasets. |
Exploring Compositional Generalization of Multimodal LLMs for Medical Imaging (2025.acl-long)
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Zhenyang Cai, Junying Chen, Rongsheng Wang, Weihong Wang, Yonglin Deng, Dingjie Song, Yize Chen, Zixu Zhang, Benyou Wang
| Challenge: | Current research suggests that multitask training outperforms single-task as different tasks can benefit each other, but they often overlook the internal relationships within these tasks. |
| Approach: | They employ compositional generalization (CG) to examine the generalization of multimodal large language models in medical imaging. |
| Outcome: | The proposed model can understand unseen medical images and is able to perform CG across classification and detection tasks. |