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README.md

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 ---
-# For reference on model card metadata, see the spec: https://github.com/huggingface/hub-docs/blob/main/datasetcard.md?plain=1
-# Doc / guide: https://huggingface.co/docs/hub/datasets-cards
-{}
+dataset: conditional-polyp-diffusion
+annotations_creators:
+- expert-generated
+language:
+- en
+license: apache-2.0
+multilinguality:
+- monolingual
+pretty_name: Conditional Polyp Diffusion
+size_categories:
+- 1K<n<10K
+source_datasets:
+- original
+task_categories:
+- image-generation
+- image-segmentation
+task_ids:
+- image-to-image-translation
+- semantic-segmentation
+paperswithcode_id: mask-conditioned-latent-diffusion
 ---
-Comming soon...!
-# Dataset Card for Dataset Name
 
-## Dataset Description
+# Dataset Card for Conditional Polyp Diffusion
 
-- **Homepage:** 
-- **Repository:** 
-- **Paper:** 
-- **Leaderboard:** 
-- **Point of Contact:** 
+## Dataset Summary
 
-### Dataset Summary
+The **Conditional Polyp Diffusion** dataset provides synthetic gastrointestinal (GI) polyp images along with segmentation masks, generated using a two-stage diffusion modeling framework. The dataset is aimed at mitigating the challenges of data scarcity and privacy in medical imaging, especially for supervised polyp segmentation tasks.
 
-This dataset card aims to be a base template for new datasets. It has been generated using [this raw template](https://github.com/huggingface/huggingface_hub/blob/main/src/huggingface_hub/templates/datasetcard_template.md?plain=1).
+- **Stage 1**: Improved diffusion model generates synthetic segmentation masks.
+- **Stage 2**: Latent diffusion model generates corresponding realistic polyp images, conditioned on the masks.
 
-### Supported Tasks and Leaderboards
+This dataset enables training and benchmarking of polyp segmentation models, improving generalizability and reducing dependence on scarce annotated real data.
 
-[More Information Needed]
+## Supported Tasks and Leaderboards
 
-### Languages
+- **Image-to-Image Translation**: Generating realistic medical images from segmentation masks.
+- **Semantic Segmentation**: Supervised training of segmentation models for polyp detection.
 
-[More Information Needed]
+## Languages
 
-## Dataset Structure
-
-### Data Instances
+The metadata and documentation are in English.
 
-[More Information Needed]
+## Dataset Structure
 
-### Data Fields
+Each sample includes:
+- A synthetic GI polyp image.
+- A corresponding segmentation mask.
 
-[More Information Needed]
+The images are generated to mimic the distribution of Kvasir-SEG masks and HyperKvasir polyp appearances.
 
-### Data Splits
+## Data Splits
 
-[More Information Needed]
+The dataset contains:
+- 1,000 synthetic masks
+- 1,000 corresponding synthetic polyp images
 
 ## Dataset Creation
 
 ### Curation Rationale
 
-[More Information Needed]
+Due to privacy and annotation constraints in medical imaging, the dataset addresses:
+- Lack of large-scale annotated datasets for polyp segmentation.
+- Need for diverse, high-fidelity training data for robust CAD systems.
 
 ### Source Data
 
-#### Initial Data Collection and Normalization
-
-[More Information Needed]
-
-#### Who are the source language producers?
-
-[More Information Needed]
+The improved diffusion model is trained on the Kvasir-SEG dataset’s segmentation masks. The conditional polyp generator is trained using these generated masks to create realistic polyp images.
 
 ### Annotations
 
-#### Annotation process
-
-[More Information Needed]
-
-#### Who are the annotators?
-
-[More Information Needed]
-
-### Personal and Sensitive Information
+- Masks are generated via diffusion models conditioned on prior distributions.
+- No manual annotations are provided; instead, generated masks are verified for similarity and diversity.
 
-[More Information Needed]
+## Usage
 
-## Considerations for Using the Data
+The dataset is intended for research in:
+- Medical image generation
+- Semi-supervised and supervised segmentation
+- Evaluation of synthetic data utility in clinical tasks
 
-### Social Impact of Dataset
+## Evaluation
 
-[More Information Needed]
+Three segmentation models (UNet++, FPN, DeepLabv3+) were trained with various combinations of real and synthetic data. Results demonstrated that using synthetic data can improve model performance, particularly with DeepLabv3+ achieving a micro-imagewise IoU of 0.7751.
 
-### Discussion of Biases
+## Citation
 
-[More Information Needed]
-
-### Other Known Limitations
-
-[More Information Needed]
-
-## Additional Information
-
-### Dataset Curators
-
-[More Information Needed]
-
-### Licensing Information
-
-[More Information Needed]
-
-### Citation Information
 ```
-@inproceedings{10.1145/3592571.3592978,
-author = {Mach\'{a}\v{c}ek, Roman and Mozaffari, Leila and Sepasdar, Zahra and Parasa, Sravanthi and Halvorsen, P\r{a}l and Riegler, Michael A. and Thambawita, Vajira},
-title = {Mask-conditioned latent diffusion for generating gastrointestinal polyp images},
-year = {2023},
-isbn = {9798400701863},
-publisher = {Association for Computing Machinery},
-address = {New York, NY, USA},
-url = {https://doi.org/10.1145/3592571.3592978},
-doi = {10.1145/3592571.3592978},
-booktitle = {Proceedings of the 4th ACM Workshop on Intelligent Cross-Data Analysis and Retrieval},
-pages = {1–9},
-numpages = {9},
-keywords = {polyp segmentation, polyp generative model, generating synthetic data, diffusion model},
-location = {Thessaloniki, Greece},
-series = {ICDAR '23}
+@inproceedings{machacek2023mask,
+  title={Mask-conditioned latent diffusion for generating gastrointestinal polyp images},
+  author={Macháček, Roman and Mozaffari, Leila and Sepasdar, Zahra and Parasa, Sravanthi and Halvorsen, Pål and Riegler, Michael A and Thambawita, Vajira},
+  booktitle={Proceedings of the 4th Workshop on Intelligent Cross-Data Analysis and Retrieval (ICDAR '23)},
+  year={2023},
+  doi={10.1145/3592571.3592978}
 }
 ```
 
-### Contributions
+## License
+
+Apache License 2.0
+
+## Dataset URL
 
-[More Information Needed]
+- Dataset: [https://huggingface.co/datasets/deepsynthbody/conditional-polyp-diffusion](https://huggingface.co/datasets/deepsynthbody/conditional-polyp-diffusion)
+- Code: [https://github.com/simulamet-host/conditional-polyp-diffusion](https://github.com/simulamet-host/conditional-polyp-diffusion)