| | --- |
| | 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) |
