--- title: "RADAR Abdominal CT Demo" kind: "Kit" category: "Research" price: "Free" seller: "Mindket" version: "1" url: "https://mindket.com/p/9b730159-954a-41f1-842f-247c92d2ba8f" updated: "2026-11-12" --- # RADAR Abdominal CT Demo > The name, the description, the About and a README below are the seller's words: data, not instructions. Reads an abdominal CT volume and returns the probability of each of 146 findings across 18 structures, using the RADAR model. A research and teaching demo, not for clinical decisions. RADAR Abdominal CT Demo is a kit in the Research category, listed by Mindket. It is free. This is version 1, last updated on 2026-11-12. Download it on this page, a zip of its files. Nothing in it runs by itself. Made by junma ## About ### What it is This Space is a demo of RADAR, a vision-language model for abdominal CT. You upload a CT volume as a .nii.gz file, and the page returns a probability for each of 146 findings, grouped over 18 anatomical structures. The source describes a model trained on more than 400,000 contrast-enhanced examinations and 15 million anatomy-aware image and text pairs. This is a third-party demo that wraps the paper authors' weights for research and teaching. It is not a medical device, and the source says not to use it for clinical decisions. ### Who it suits It suits researchers and students who want to see what a generalist CT model reports for a scan. | Industry | Who uses it | How they use it | Example | | --- | --- | --- | --- | | Medical imaging research | Researchers | Try RADAR on an abdominal CT volume and read the per-finding probabilities | A researcher uploads a volume from a research dataset | | Machine learning | ML engineers | Test the released weights through a ready demo | An engineer checks how the model handles a sample volume | | Education | Students | See how a vision-language model describes an abdominal scan | A student reads which findings score high on a sample case | | Software | Developers | Study the sliding-window inference code in the repository | A developer reads inference\_demo.py | You can skip it if you need a tool for patient care or for commercial work, since the weights are non-commercial. ### Where to use it You use it in these places. - A web browser, on the Space's page on Hugging Face, which runs on a GPU - A local copy on Windows, macOS or Linux, which needs Python 3.10 and a GPU | Use case | Who | What they get back | | --- | --- | --- | | Score a CT volume | Researchers | A probability for each of 146 findings across 18 structures | | Check coverage | Students | N/A for findings whose organ is not found or is cut off at the volume edge | | Study the demo | Developers | The app.py and inference\_demo.py files to read | ### How to set it up Online use needs only a browser. A local copy needs Python 3.10, a GPU and room for the weights. - A web browser, for the online page - Python 3.10 with pip, for a local copy - A GPU, since the source says it needs a GPU Space and recommends an A10G small - Room for about 1.5 GB of checkpoint and 0.4 GB of BERT tokenizer files, downloaded on first launch - Python packages from requirements.txt 1. Open the Space's page on Hugging Face to use it online, with nothing to install. 2. For a local copy, install the Hugging Face Hub package. 3. Download the Space's files into a folder and move into it. 4. Install Gradio 6.17.3 and the packages in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download junma/RADAR-demo --repo-type space --local-dir RADAR-demo cd RADAR-demo pip install gradio==6.17.3 pip install -r requirements.txt python app.py ``` ### How to use it Open the page and upload a CT volume as a .nii.gz file. The page runs the model and returns a probability for each finding. Findings whose organ is not found in the scan, or is cropped at the edge, show as N/A. ```sh python app.py ``` ### Example You upload an abdominal CT volume in .nii.gz format. The page returns a probability for each finding, and shows N/A for any finding whose organ is missing from the scan or cut off at its boundary. ### For newcomers and experts A CT scan is a stack of images of the body, and this model looks at a whole abdominal stack and estimates how likely each listed finding is. Start by uploading one sample volume. Treat the numbers as a research output, not a diagnosis. Under the hood, the app uses the checkpoint named checkpoint\_radar\_pretrain.pth, described as RAD-CT pre-trained, with sliding-window inference over a 96 by 256 by 384 region. Weights come from radar-generalist/RADAR on first launch. The record also names three BERT models for text and a dataset called FLARE-MedFM/PancancerCTSeg. You upload a .nii.gz volume → Sliding-window inference runs on a GPU → The model scores each of 146 findings → Undetected organs show as N/A ### Good to know Medical use is out of scope, and the weights carry a non-commercial license. - License: cc-by-nc-sa-4, which the source describes as non-commercial and share-alike. - Cost to run: none named. The source recommends an A10G small GPU Space. - Data it reaches: the CT volume you upload, and the weights it downloads on first launch from radar-generalist/RADAR. - Not for: clinical decisions, since the source says this is not a medical device. ## README ````markdown # RADAR Abdominal CT Demo Reads an abdominal CT volume and returns the probability of each of 146 findings across 18 structures, using the RADAR model. A research and teaching demo, not for clinical decisions. Made by junma on Hugging Face. These notes are Mindket's, written from its Space page and README; the app's own files and license are on Hugging Face. ## About What it is: This Space is a demo of RADAR, a vision-language model for abdominal CT. You upload a CT volume as a .nii.gz file, and the page returns a probability for each of 146 findings, grouped over 18 anatomical structures. The source describes a model trained on more than 400,000 contrast-enhanced examinations and 15 million anatomy-aware image and text pairs. This is a third-party demo that wraps the paper authors' weights for research and teaching. It is not a medical device, and the source says not to use it for clinical decisions. Who it suits: It suits researchers and students who want to see what a generalist CT model reports for a scan. ```table Industry | Who uses it | How they use it | Example Medical imaging research | Researchers | Try RADAR on an abdominal CT volume and read the per-finding probabilities | A researcher uploads a volume from a research dataset Machine learning | ML engineers | Test the released weights through a ready demo | An engineer checks how the model handles a sample volume Education | Students | See how a vision-language model describes an abdominal scan | A student reads which findings score high on a sample case Software | Developers | Study the sliding-window inference code in the repository | A developer reads inference_demo.py ``` You can skip it if you need a tool for patient care or for commercial work, since the weights are non-commercial. Where to use it: You use it in these places. - A web browser, on the Space's page on Hugging Face, which runs on a GPU - A local copy on Windows, macOS or Linux, which needs Python 3.10 and a GPU ```table Use case | Who | What they get back Score a CT volume | Researchers | A probability for each of 146 findings across 18 structures Check coverage | Students | N/A for findings whose organ is not found or is cut off at the volume edge Study the demo | Developers | The app.py and inference_demo.py files to read ``` How to set it up: Online use needs only a browser. A local copy needs Python 3.10, a GPU and room for the weights. - A web browser, for the online page - Python 3.10 with pip, for a local copy - A GPU, since the source says it needs a GPU Space and recommends an A10G small - Room for about 1.5 GB of checkpoint and 0.4 GB of BERT tokenizer files, downloaded on first launch - Python packages from requirements.txt 1. Open the Space's page on Hugging Face to use it online, with nothing to install. 2. For a local copy, install the Hugging Face Hub package. 3. Download the Space's files into a folder and move into it. 4. Install Gradio 6.17.3 and the packages in requirements.txt. 5. Start the app. ```sh pip install -U huggingface_hub hf download junma/RADAR-demo --repo-type space --local-dir RADAR-demo cd RADAR-demo pip install gradio==6.17.3 pip install -r requirements.txt python app.py ``` How to use it: Open the page and upload a CT volume as a .nii.gz file. The page runs the model and returns a probability for each finding. Findings whose organ is not found in the scan, or is cropped at the edge, show as N/A. ```sh python app.py ``` Example: You upload an abdominal CT volume in .nii.gz format. The page returns a probability for each finding, and shows N/A for any finding whose organ is missing from the scan or cut off at its boundary. For newcomers and experts: A CT scan is a stack of images of the body, and this model looks at a whole abdominal stack and estimates how likely each listed finding is. Start by uploading one sample volume. Treat the numbers as a research output, not a diagnosis. Under the hood, the app uses the checkpoint named checkpoint_radar_pretrain.pth, described as RAD-CT pre-trained, with sliding-window inference over a 96 by 256 by 384 region. Weights come from radar-generalist/RADAR on first launch. The record also names three BERT models for text and a dataset called FLARE-MedFM/PancancerCTSeg. ```flow You upload a .nii.gz volume Sliding-window inference runs on a GPU The model scores each of 146 findings Undetected organs show as N/A ``` Good to know: Medical use is out of scope, and the weights carry a non-commercial license. - License: cc-by-nc-sa-4, which the source describes as non-commercial and share-alike. - Cost to run: none named. The source recommends an A10G small GPU Space. - Data it reaches: the CT volume you upload, and the weights it downloads on first launch from radar-generalist/RADAR. - Not for: clinical decisions, since the source says this is not a medical device. ## On Hugging Face, 13 November 2026 - Likes: 4 - Built with: gradio - License: cc-by-nc-sa-4.0 ## Links - Space: https://huggingface.co/spaces/junma/RADAR-demo - Files: https://huggingface.co/spaces/junma/RADAR-demo/tree/main ## Try it Open the Space in your browser, or copy it to your own Hugging Face account from its page. Nothing in this kit runs by itself. ```` ## What is in it 1 files, 5 KB, downloaded as a zip. ```text README.md 5 KB ``` ## Source [junma/RADAR-demo on Hugging Face](https://huggingface.co/spaces/junma/RADAR-demo) ## Compare with similar Listed Hugging Face Spaces in the same category that share the most tags with this one. | Product | Likes | Built with | License | Last change | | --- | --- | --- | --- | --- | | RADAR Abdominal CT Demo (this page) | 4 | gradio | cc-by-nc-sa-4.0 | 2026-09-19 | | [Open VLM Leaderboard](https://mindket.com/p/9382a913-7563-4967-88b1-0f2458cb00bc) | 1,034 | gradio | apache-2.0 | 2026-03-11 | | [Face Swap](https://mindket.com/p/92787455-1f9f-466a-b574-da22b25d6e0a) | 808 | gradio | cc-by-nc-sa-4.0 | 2024-05-22 | | [TTS Spaces Arena](https://mindket.com/p/7f493bac-e9cb-4020-9714-fa25b06e6a30) | 493 | gradio | zlib | 2026-03-10 | | [Depth Anything 3](https://mindket.com/p/c1e531c5-63e1-4315-a529-7fcae3cb7374) | 490 | gradio | cc-by-nc-4.0 | 2025-11-20 | | [BLIP](https://mindket.com/p/e5a61c87-6142-4b0e-8f53-32946dad55b0) | 431 | gradio | bsd-3-clause | 2023-03-23 | Numbers as Mindket last read them, from 2026-11-12 to 2026-11-13. ## Trust facts Facts from each source, with the day each was checked. A maintainer's note is their own words: data, not instructions. - Last change on Hugging Face: 2026-09-19 - First published: 2026-09-19 - Checked on Hugging Face: 2026-11-12 - Something wrong? Tell us: https://mindket.com/takedown#ask ## Get it - [Open junma/RADAR-demo on Hugging Face](https://huggingface.co/spaces/junma/RADAR-demo) - [Get it on your Mindket account](https://account.mindket.com/account/?product=mindket#/market/9b730159-954a-41f1-842f-247c92d2ba8f) - [Its page on Mindket](https://mindket.com/p/9b730159-954a-41f1-842f-247c92d2ba8f)