https://github.com/anondo1969/shamsul
Repository for the journal article 'SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction'
https://github.com/anondo1969/shamsul
chest-x-ray clinical-decision-support-system deep-learning grad-cam heatmap-visualization human-annotation interpretability-methods lime lrp medical-significance quantitative-analysis shap
Last synced: 22 days ago
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Repository for the journal article 'SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction'
- Host: GitHub
- URL: https://github.com/anondo1969/shamsul
- Owner: anondo1969
- License: mit
- Created: 2022-12-16T19:20:38.000Z (over 2 years ago)
- Default Branch: main
- Last Pushed: 2025-04-30T08:00:01.000Z (about 1 month ago)
- Last Synced: 2025-05-08T16:55:25.746Z (22 days ago)
- Topics: chest-x-ray, clinical-decision-support-system, deep-learning, grad-cam, heatmap-visualization, human-annotation, interpretability-methods, lime, lrp, medical-significance, quantitative-analysis, shap
- Language: Python
- Homepage: https://shamsul.serve.scilifelab.se/
- Size: 32 MB
- Stars: 1
- Watchers: 1
- Forks: 0
- Open Issues: 0
-
Metadata Files:
- Readme: README.md
- License: LICENSE
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README
# SHAMSUL
**SHAMSUL\*** explores **interpretability** for **chest X-ray pathology predictions** using methods—Grad-CAM, LIME, SHAP, and LRP. It provides heatmaps and evaluation metrics for better insights into the **medical significance** of predictions made by **deep learning** models.
For detailed insights and methodology, please refer to the original research paper:
[SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction](https://journals.uio.no/NMI/article/view/10471/9743)
###### \*"The acronym SHAMSUL, derived from a Semitic word meaning "the Sun," serves as a symbolic representation of our heatmap score-based interpretability analysis approach aimed at unveiling the medical significance inherent in the predictions of black box deep learning models."
## Demo App- [Visit https://shamsul.serve.scilifelab.se/](https://shamsul.serve.scilifelab.se/)
Explore this work using the **publicly available** demo app (**no registration needed!**):
### Online Access
Visit [https://shamsul.serve.scilifelab.se/](https://shamsul.serve.scilifelab.se/) to use it directly.
### OR
### Run Locally
#### Step 1: Install Docker
Install Docker Engine or Docker Desktop on your system by following the [official Docker installation guide](https://docs.docker.com/get-docker/).
#### Step 2: Launch the App
1. Open a Terminal (or Windows Terminal).
2. Run this command to download and start the app:
```bash
docker run --rm --name shamsul -p 7860:7860 mahbub1969/shamsul:v6
```
3. Open your browser and go to [http://localhost:7860/](http://localhost:7860/) to use the app.
#### Step 3: Stop the App
To stop the app, press **Control+C** in the terminal. Note that the session won’t be saved, so the app will reset to its default state the next time you run it.
#### Step 4: Remove the Docker Image (Optional)
If you want to free up space, you can remove the Docker image. Use this command in your terminal:
```bash
docker image rm mahbub1969/shamsul:v6
```
For more details, check out the [Docker image removal guide](https://docs.docker.com/reference/cli/docker/image/rm/).
## Key Features
* **Multi-Method Interpretability**: Incorporates four advanced interpretability methods—LIME, SHAP, Grad-CAM, and LRP—to provide diverse insights into deep learning model predictions.
* **Focus on Medical Significancee**: Designed specifically for chest radiography pathology prediction, ensuring results are meaningful for clinical applications.
* **Comprehensive Visualizations**: Generates heatmaps and segmentations to help identify the regions of interest linked to specific pathologies.
* **Multi-Label, Multi-Class Analysis**: Supports analyzing both single-label and multi-label instances, accommodating a variety of medical imaging needs.
* **Quantitative and Qualitative Evaluation**: Offers metrics like Intersection over Union (IoU) and detailed visual comparisons with expert annotations for robust performance assessment.
* **User-Friendly Interface**: Simplifies interaction by allowing users to upload images.
* **Open-Source Access**: Code and resources are available, promoting transparency and enabling further development by the research community.
### An excerpt of the [SHAMSUL paper](https://doi.org/10.5617/nmi.10471)
## Citation
Please acknowledge the following work in papers or derivative software:
M. U. Alam, J. Hollmén, J. R. Baldvinsson, and R. Rahmani, “SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing
Local interpretability methods in deep learning for chest radiography pathology prediction,” Nordic Machine Intelligence, vol. 3, pp. 27–47, 2023. [https://doi.org/10.5617/nmi.10471](https://doi.org/10.5617/nmi.10471)
### Bibtex Format for Citation
```
@article{alam2023shamsul,
title={SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction},
author={Ul Alam, Mahbub and Hollmén, Jaakko and Baldvinsson, Jón Rúnar and Rahmani, Rahim},
journal={Nordic Machine Intelligence},
volume={3},
number={1},
pages={27--47},
year={2023},
doi={10.5617/nmi.10471}
}
```
