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https://github.com/zhjohnchan/awesome-radiology-report-generation
A curated list of radiology report generation (medical report generation) and related areas. :-)
https://github.com/zhjohnchan/awesome-radiology-report-generation
List: awesome-radiology-report-generation
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A curated list of radiology report generation (medical report generation) and related areas. :-)
- Host: GitHub
- URL: https://github.com/zhjohnchan/awesome-radiology-report-generation
- Owner: zhjohnchan
- Created: 2020-02-02T15:03:41.000Z (over 4 years ago)
- Default Branch: master
- Last Pushed: 2022-05-07T06:19:31.000Z (over 2 years ago)
- Last Synced: 2024-05-21T16:08:58.435Z (4 months ago)
- Homepage:
- Size: 38.1 KB
- Stars: 164
- Watchers: 6
- Forks: 22
- Open Issues: 1
-
Metadata Files:
- Readme: README.md
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- ultimate-awesome - awesome-radiology-report-generation - A curated list of radiology report generation (medical report generation) and related areas. :-). (Other Lists / PowerShell Lists)
README
# Awesome Radiology Report Generation[](https://awesome.re)
A curated list of radiology report generation (medical report generation) and related areas. :-)
## Contributing
Please feel free to send me [pull requests](https://github.com/zhjohnchan/awesome-medical-report-generation/pulls) or email ([email protected]) to add links or to discuss with me about this area.
Markdown format:
```markdown
- [Paper Name](link) - Author 1 et al, `Conference Year`. [[code]](link)
```
## Table of Contents
- [Papers](#papers)
- [Survey](#survey)
- [2016](#2016) - [2017](#2017) - [2018](#2018) - [2019](#2019) - [2020](#2020) - [2021](#2021)
- [Dataset](#dataset)
## Papers
### Survey
* [A Survey on Biomedical Image Captioning](https://arxiv.org/pdf/1905.13302) - Kougia V et al, `arXiv preprint 2019`.
* [Deep learning in generating radiology reports: A survey](https://www.sciencedirect.com/science/article/pii/S0933365719302635?casa_token=r5ldLjKGr9gAAAAA:f4qB7XVPFx9BAug8xI09K_Na82cg4torsrelJ89J3uBZJBl251CTGcRghkoY_kIAbz9ne8pJU3AJ) - Monshi et al, `arXiv preprint 2020`.
* [Diagnostic Captioning: A Survey](https://arxiv.org/pdf/2101.07299) - Pavlopoulos et al, `arXiv preprint 2021`.
### 2016
* [Learning to read chest x-rays: Recurrent neural cascade model for automated image annotation](http://openaccess.thecvf.com/content_cvpr_2016/papers/Shin_Learning_to_Read_CVPR_2016_paper.pdf) - Shin H C et al, `CVPR 2016`.
### 2017
* [Mdnet: A semantically and visually interpretable medical image diagnosis network](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_MDNet_A_Semantically_CVPR_2017_paper.pdf) - Zhang Z et al, `CVPR 2017`.
* [Chestx-ray8: Hospital-scale chest x-ray database and benchmarks on weakly-supervised classification and localization of common thorax diseases](http://openaccess.thecvf.com/content_cvpr_2017/papers/Wang_ChestX-ray8_Hospital-Scale_Chest_CVPR_2017_paper.pdf) - Wang X et al, `CVPR 2017`.
* [Tandemnet: Distilling knowledge from medical images using diagnostic reports as optional semantic references](http://openaccess.thecvf.com/content_cvpr_2017/papers/Zhang_MDNet_A_Semantically_CVPR_2017_paper.pdf) - Zhang Z et al, `MICCAI 2017`.
### 2018
* [On the automatic generation of medical imaging reports](https://arxiv.org/pdf/1711.08195) - Jing B et al, `ACL 2018`.
* [Textray: Mining clinical reports to gain a broad understanding of chest x-rays](https://arxiv.org/pdf/1806.02121) - Laserson J et al, `MICCAI 2018`.
* [Hybrid retrieval-generation reinforced agent for medical image report generation](http://papers.nips.cc/paper/7426-hybrid-retrieval-generation-reinforced-agent-for-medical-image-report-generation.pdf) - Li Y et al, `NIPS 2018`.
### 2019
* [Show, Describe and Conclude: On Exploiting the Structure Information of Chest X-ray Reports](https://www.aclweb.org/anthology/P19-1657.pdf) - Jing B et al, `ACL 2019`.
* [Automatic radiology report generation based on multi-view image fusion and medical concept enrichment](https://arxiv.org/pdf/1907.09085) - Yuan et al, `MICCA 2019`.
* [Addressing data bias problems for chest x-ray image report generation](https://arxiv.org/pdf/1908.02123) - Harzig P et al, `BMVC 2019`.
* [Knowledge-driven encode, retrieve, paraphrase for medical image report generation](https://www.aaai.org/ojs/index.php/AAAI/article/download/4637/4515) - Li C Y et al, `AAAI 2019`.
* [Automatic Generation of Medical Imaging Diagnostic Report with Hierarchical Recurrent Neural Network](https://ieeexplore.ieee.org/iel7/8961330/8970627/08970668.pdf?casa_token=zMmkGsvlcI8AAAAA:SbNyODTWZI1l5kNG_E6SkOs2r5HMhKrGnu8B1CoxPB7kuvtZmvxS7KIoaZMPA2pysI6VcvmBJ426cQ) - Yin et al, `ICDM 2019`.
* [EEGtoText: Learning to Write Medical Reports from EEG Recordings](http://proceedings.mlr.press/v106/biswal19a/biswal19a.pdf) - Biswal S et al, `PMLR 2019`.
* [Multi-Attention and Incorporating Background Information Model for Chest X-Ray Image Report Generation](https://ieeexplore.ieee.org/iel7/6287639/8600701/08867873.pdf) - Huang X et al, `ACCESS 2019`.
* [Baselines for Chest X-Ray Report Generation](https://ml4health.github.io/2019/pdf/175_ml4h_preprint.pdf) - Boag W et al, `arXiv preprint 2019`.
* [Clinically accurate chest X-ray report generation](https://arxiv.org/pdf/1904.02633) - Liu G et al, `arXiv preprint 2019`.
* [Optimizing the Factual Correctness of a Summary: A Study of Summarizing Radiology Reports](https://arxiv.org/pdf/1911.02541) - Zhang Y et al, `arXiv preprint 2019`.
### 2020
* [Generating Radiology Reports via Memory-driven Transformer](https://www.aclweb.org/anthology/2020.emnlp-main.112.pdf) - Chen Z et al, `EMNLP 2020`. [[code]](https://github.com/zhjohnchan/R2Gen)
* [When Radiology Report Generation Meets Knowledge Graph](https://ojs.aaai.org/index.php/AAAI/article/view/6989/6843) - Zhang Y et al, `AAAI 2020`.
### 2021
* [Exploring and Distilling Posterior and Prior Knowledge for Radiology Report Generation](https://openaccess.thecvf.com/content/CVPR2021/papers/Liu_Exploring_and_Distilling_Posterior_and_Prior_Knowledge_for_Radiology_Report_CVPR_2021_paper.pdf) - Liu F et al, `CVPR 2021`.
* [A Self-Boosting Framework for Automated Radiographic Report Generation](https://openaccess.thecvf.com/content/CVPR2021/papers/Wang_A_Self-Boosting_Framework_for_Automated_Radiographic_Report_Generation_CVPR_2021_paper.pdf) - Wang Z et al, `CVPR 2021`.
* [Weakly Supervised Contrastive Learning for Chest X-Ray Report Generation](https://aclanthology.org/2021.findings-emnlp.336.pdf) - Yan A et al, `EMNLP 2021, findings`. [[code]](https://github.com/zzxslp/WCL)
## Dataset
* [Preparing a collection of radiology examinations for distribution and retrieval](https://academic.oup.com/jamia/article/23/2/304/2572395) - Demner-Fushman D et al, `JAMIA 2016`.
* [MIMIC-CXR: a large publicly available database of labeled chest radiographs](https://deepai.org/publication/mimic-cxr-a-large-publicly-available-database-of-labeled-chest-radiographs) - Johnson A E W et al, `arXiv preprint 2019`.
* [Padchest: A large chest x-ray image dataset with multi-label annotated reports](https://arxiv.org/pdf/1901.07441) - Bustos A et al, `arXiv preprint 2019`.
## Popular Implementations
### PyTorch
* [zhjohnchan/R2Gen](https://github.com/zhjohnchan/R2Gen)
* [zhjohnchan/R2GenCMN](https://github.com/zhjohnchan/R2GenCMN)
## Licenses
[](http://creativecommons.org/publicdomain/zero/1.0/)
To the extent possible under law, [Zhihong Chen](https://github.com/zhjohnchan) has waived all copyright and related or neighboring rights to this work.