{"id":27991164,"url":"https://github.com/anondo1969/shamsul","last_synced_at":"2025-06-12T03:41:54.123Z","repository":{"id":67959695,"uuid":"579144669","full_name":"anondo1969/SHAMSUL","owner":"anondo1969","description":"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'","archived":false,"fork":false,"pushed_at":"2025-04-30T08:00:01.000Z","size":33550,"stargazers_count":1,"open_issues_count":0,"forks_count":0,"subscribers_count":1,"default_branch":"main","last_synced_at":"2025-05-08T16:55:25.746Z","etag":null,"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"],"latest_commit_sha":null,"homepage":"https://shamsul.serve.scilifelab.se/","language":"Python","has_issues":true,"has_wiki":null,"has_pages":null,"mirror_url":null,"source_name":null,"license":"mit","status":null,"scm":"git","pull_requests_enabled":true,"icon_url":"https://github.com/anondo1969.png","metadata":{"files":{"readme":"README.md","changelog":null,"contributing":null,"funding":null,"license":"LICENSE","code_of_conduct":null,"threat_model":null,"audit":null,"citation":null,"codeowners":null,"security":null,"support":null,"governance":null,"roadmap":null,"authors":null,"dei":null,"publiccode":null,"codemeta":null,"zenodo":null}},"created_at":"2022-12-16T19:20:38.000Z","updated_at":"2025-04-30T08:00:04.000Z","dependencies_parsed_at":"2023-04-13T02:13:45.896Z","dependency_job_id":"162e0120-da35-4530-b8a2-c4715b4b4312","html_url":"https://github.com/anondo1969/SHAMSUL","commit_stats":null,"previous_names":[],"tags_count":0,"template":false,"template_full_name":null,"repository_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anondo1969%2FSHAMSUL","tags_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anondo1969%2FSHAMSUL/tags","releases_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anondo1969%2FSHAMSUL/releases","manifests_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories/anondo1969%2FSHAMSUL/manifests","owner_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners/anondo1969","download_url":"https://codeload.github.com/anondo1969/SHAMSUL/tar.gz/refs/heads/main","host":{"name":"GitHub","url":"https://github.com","kind":"github","repositories_count":253112077,"owners_count":21856070,"icon_url":"https://github.com/github.png","version":null,"created_at":"2022-05-30T11:31:42.601Z","updated_at":"2022-07-04T15:15:14.044Z","host_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub","repositories_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repositories","repository_names_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/repository_names","owners_url":"https://repos.ecosyste.ms/api/v1/hosts/GitHub/owners"}},"keywords":["chest-x-ray","clinical-decision-support-system","deep-learning","grad-cam","heatmap-visualization","human-annotation","interpretability-methods","lime","lrp","medical-significance","quantitative-analysis","shap"],"created_at":"2025-05-08T16:55:30.332Z","updated_at":"2025-05-08T16:55:30.853Z","avatar_url":"https://github.com/anondo1969.png","language":"Python","funding_links":[],"categories":[],"sub_categories":[],"readme":"\n\u003cdiv align=\"center\"\u003e\n   \u003cfigure\u003e\n  \u003cimg src=\"https://mahbub.blogs.dsv.su.se/files/2025/04/SHAMSUL.gif\"\u003e\n   \u003cfigcaption\u003e\u003ca href=\"https://shamsul.serve.scilifelab.se\"\u003e\u003cbr\u003eSHAMSUL Demo App- Visit https://shamsul.serve.scilifelab.se\u003c/a\u003e\u003c/figcaption\u003e\n   \u003c/figure\u003e\n\u003c/div\u003e\n\n# SHAMSUL\n\n**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.\n\nFor detailed insights and methodology, please refer to the original research paper:  \n[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)  \n\n###### \\*\"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.\"\n\n## Demo App- [Visit https://shamsul.serve.scilifelab.se/](https://shamsul.serve.scilifelab.se/)\n\nExplore this work using the **publicly available** demo app (**no registration needed!**):  \n\n### Online Access\nVisit [https://shamsul.serve.scilifelab.se/](https://shamsul.serve.scilifelab.se/) to use it directly.  \n\n### OR\n\n### Run Locally \n\n#### Step 1: Install Docker  \nInstall Docker Engine or Docker Desktop on your system by following the [official Docker installation guide](https://docs.docker.com/get-docker/).  \n\n#### Step 2: Launch the App  \n1. Open a Terminal (or Windows Terminal).  \n2. Run this command to download and start the app:  \n   ```bash\n   docker run --rm --name shamsul -p 7860:7860 mahbub1969/shamsul:v6\n   ```  \n3. Open your browser and go to [http://localhost:7860/](http://localhost:7860/) to use the app.  \n\n#### Step 3: Stop the App  \nTo 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.  \n\n#### Step 4: Remove the Docker Image (Optional)  \nIf you want to free up space, you can remove the Docker image. Use this command in your terminal:  \n```bash\ndocker image rm mahbub1969/shamsul:v6\n```  \nFor more details, check out the [Docker image removal guide](https://docs.docker.com/reference/cli/docker/image/rm/).\n\n## Key Features\n\n*   **Multi-Method Interpretability**: Incorporates four advanced interpretability methods—LIME, SHAP, Grad-CAM, and LRP—to provide diverse insights into deep learning model predictions.\n    \n*   **Focus on Medical Significancee**: Designed specifically for chest radiography pathology prediction, ensuring results are meaningful for clinical applications.\n    \n*   **Comprehensive Visualizations**: Generates heatmaps and segmentations to help identify the regions of interest linked to specific pathologies.\n    \n*   **Multi-Label, Multi-Class Analysis**: Supports analyzing both single-label and multi-label instances, accommodating a variety of medical imaging needs.\n    \n*   **Quantitative and Qualitative Evaluation**: Offers metrics like Intersection over Union (IoU) and detailed visual comparisons with expert annotations for robust performance assessment.\n        \n*   **User-Friendly Interface**: Simplifies interaction by allowing users to upload images.\n    \n*   **Open-Source Access**: Code and resources are available, promoting transparency and enabling further development by the research community.\n\n\n### An excerpt of the [SHAMSUL paper](https://doi.org/10.5617/nmi.10471)\n\n\n![An excerpt of the paper](https://raw.githubusercontent.com/anondo1969/SHAMSUL/main/codes/excerpt.png)\n\n## Citation\n\nPlease acknowledge the following work in papers or derivative software:\n\nM. U. Alam, J. Hollmén, J. R. Baldvinsson, and R. Rahmani, “SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing\nLocal 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)\n\n### Bibtex Format for Citation\n\n```\n@article{alam2023shamsul,\n  title={SHAMSUL: Systematic Holistic Analysis to investigate Medical Significance Utilizing Local interpretability methods in deep learning for chest radiography pathology prediction},\n  author={Ul Alam, Mahbub and Hollmén, Jaakko and Baldvinsson, Jón Rúnar and Rahmani, Rahim},\n  journal={Nordic Machine Intelligence},\n  volume={3},\n  number={1},\n  pages={27--47},\n  year={2023},\n  doi={10.5617/nmi.10471}\n}\n```\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanondo1969%2Fshamsul","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fanondo1969%2Fshamsul","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fanondo1969%2Fshamsul/lists"}