| --- |
| 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 |
| --- |
| |
| # Dataset Card for Conditional Polyp Diffusion |
|
|
| ## 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. |
|
|
| - **Stage 1**: Improved diffusion model generates synthetic segmentation masks. |
| - **Stage 2**: Latent diffusion model generates corresponding realistic polyp images, conditioned on the masks. |
|
|
| This dataset enables training and benchmarking of polyp segmentation models, improving generalizability and reducing dependence on scarce annotated real data. |
|
|
| ## Supported Tasks and Leaderboards |
|
|
| - **Image-to-Image Translation**: Generating realistic medical images from segmentation masks. |
| - **Semantic Segmentation**: Supervised training of segmentation models for polyp detection. |
|
|
| ## Languages |
|
|
| The metadata and documentation are in English. |
|
|
| ## Dataset Structure |
|
|
| Each sample includes: |
| - A synthetic GI polyp image. |
| - A corresponding segmentation mask. |
|
|
| The images are generated to mimic the distribution of Kvasir-SEG masks and HyperKvasir polyp appearances. |
|
|
| ## Data Splits |
|
|
| The dataset contains: |
| - 1,000 synthetic masks |
| - 1,000 corresponding synthetic polyp images |
|
|
| ## Dataset Creation |
|
|
| ### Curation Rationale |
|
|
| 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 |
|
|
| 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 |
|
|
| - Masks are generated via diffusion models conditioned on prior distributions. |
| - No manual annotations are provided; instead, generated masks are verified for similarity and diversity. |
|
|
| ## Usage |
|
|
| The dataset is intended for research in: |
| - Medical image generation |
| - Semi-supervised and supervised segmentation |
| - Evaluation of synthetic data utility in clinical tasks |
|
|
| ## Evaluation |
|
|
| 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. |
|
|
| ## Citation |
|
|
| ``` |
| @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} |
| } |
| ``` |
|
|
| ## License |
|
|
| Apache License 2.0 |
|
|
| ## Dataset URL |
|
|
| - 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) |
