Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection
Paper • 2508.00438 • Published
Dataset Preview
The full dataset viewer is not available (click to read why). Only showing a preview of the rows.
The dataset generation failed
Error code: DatasetGenerationError
Exception: IndexError
Message: list index out of range
Traceback: Traceback (most recent call last):
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1901, in _prepare_split_single
original_shard_lengths[original_shard_id] += len(table)
~~~~~~~~~~~~~~~~~~~~~~^^^^^^^^^^^^^^^^^^^
IndexError: list index out of range
The above exception was the direct cause of the following exception:
Traceback (most recent call last):
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 1347, in compute_config_parquet_and_info_response
parquet_operations = convert_to_parquet(builder)
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/src/services/worker/src/worker/job_runners/config/parquet_and_info.py", line 980, in convert_to_parquet
builder.download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 884, in download_and_prepare
self._download_and_prepare(
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 947, in _download_and_prepare
self._prepare_split(split_generator, **prepare_split_kwargs)
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1739, in _prepare_split
for job_id, done, content in self._prepare_split_single(
^^^^^^^^^^^^^^^^^^^^^^^^^^^
File "/usr/local/lib/python3.12/site-packages/datasets/builder.py", line 1922, in _prepare_split_single
raise DatasetGenerationError("An error occurred while generating the dataset") from e
datasets.exceptions.DatasetGenerationError: An error occurred while generating the datasetNeed help to make the dataset viewer work? Make sure to review how to configure the dataset viewer, and open a discussion for direct support.
text string |
|---|
0 0.474017 0.315267 0.078351 0.078351 |
0 0.149139 0.390308 0.124096 0.124096 |
1 0.336285 0.361426 0.093915 0.093915 |
0 0.395384 0.273502 0.095472 0.095472 |
0 0.385472 0.521240 0.088947 0.088947 |
0 0.051376 0.566972 0.106422 0.106422 |
0 0.203669 0.791743 0.097248 0.097248 |
0 0.297318 0.309516 0.106428 0.106428 |
0 0.436293 0.104599 0.073985 0.073985 |
0 0.468799 0.174508 0.079479 0.079479 |
0 0.494495 0.210092 0.064220 0.064220 |
0 0.700525 0.351376 0.064220 0.064220 |
1 0.396620 0.197302 0.108257 0.108257 |
0 0.567865 0.174698 0.107815 0.107815 |
0 0.580182 0.072349 0.089045 0.089045 |
0 0.605827 0.223926 0.112345 0.112345 |
0 0.292929 0.190236 0.111111 0.111111 |
0 0.322936 0.362385 0.100917 0.100917 |
0 0.371947 0.302228 0.118655 0.118655 |
0 0.524220 0.318350 0.114984 0.114984 |
0 0.686668 0.479184 0.073394 0.073394 |
0 0.501344 0.219653 0.054624 0.054624 |
0 0.387205 0.388890 0.084175 0.084175 |
1 0.455045 0.168807 0.073394 0.073394 |
0 0.157798 0.316514 0.150459 0.150459 |
1 0.347656 0.412544 0.134667 0.134667 |
0 0.513869 0.253946 0.082608 0.082608 |
1 0.544552 0.298790 0.080248 0.080248 |
1 0.367496 0.511528 0.123134 0.123134 |
0 0.491744 0.234862 0.095413 0.095413 |
0 0.476147 0.200917 0.122936 0.122936 |
0 0.266972 0.072477 0.064220 0.064220 |
1 0.273451 0.171374 0.098567 0.098567 |
0 0.363382 0.226325 0.096888 0.096888 |
0 0.334456 0.252939 0.092694 0.092694 |
0 0.453346 0.499587 0.068513 0.068513 |
0 0.554830 0.599768 0.099421 0.099421 |
0 0.497640 0.516906 0.141598 0.141598 |
0 0.475251 0.533976 0.071309 0.071309 |
0 0.503863 0.490309 0.090249 0.090249 |
0 0.556959 0.323526 0.073548 0.073548 |
0 0.232110 0.416224 0.174312 0.174312 |
0 0.240366 0.120183 0.137615 0.137615 |
1 0.448972 0.247631 0.124288 0.124288 |
0 0.457445 0.209968 0.080975 0.080975 |
0 0.463132 0.190391 0.096368 0.096368 |
1 0.230332 0.480948 0.128528 0.128528 |
0 0.483487 0.127523 0.097248 0.097248 |
1 0.323538 0.268246 0.109287 0.109287 |
1 0.631348 0.326415 0.107395 0.107395 |
1 0.280287 0.228947 0.096667 0.096667 |
0 0.447777 0.309034 0.088667 0.088667 |
1 0.385018 0.213873 0.096555 0.096555 |
0 0.489099 0.269006 0.069969 0.069969 |
0 0.338532 0.328440 0.102752 0.102752 |
0 0.186869 0.468014 0.111111 0.111111 |
0 0.559633 0.227523 0.091743 0.091743 |
0 0.372476 0.314679 0.117430 0.117430 |
0 0.702752 0.251376 0.073394 0.073394 |
1 0.644954 0.172477 0.082569 0.082569 |
0 0.655962 0.125688 0.064220 0.064220 |
1 0.353890 0.226674 0.106439 0.106439 |
1 0.274496 0.282541 0.142948 0.142948 |
0 0.358716 0.177982 0.089908 0.089908 |
0 0.394598 0.390154 0.103963 0.103963 |
0 0.447454 0.539900 0.092950 0.092950 |
0 0.488991 0.377982 0.086238 0.086238 |
0 0.690946 0.571193 0.056639 0.056639 |
0 0.556973 0.299867 0.088587 0.088587 |
1 0.586796 0.297290 0.072514 0.072514 |
0 0.260550 0.187156 0.121101 0.121101 |
0 0.242202 0.377064 0.106421 0.106421 |
1 0.494395 0.448015 0.096183 0.096183 |
1 0.683622 0.492748 0.061326 0.061326 |
0 0.501835 0.265138 0.119266 0.119266 |
1 0.382155 0.585861 0.164983 0.164983 |
1 0.382155 0.735689 0.084175 0.084175 |
1 0.342202 0.210092 0.144954 0.144954 |
0 0.124219 0.512599 0.057285 0.057285 |
0 0.541685 0.561014 0.076745 0.076745 |
1 0.446475 0.750498 0.090743 0.090743 |
0 0.239450 0.477982 0.093578 0.093578 |
1 0.253211 0.221101 0.051376 0.051376 |
1 0.253665 0.615234 0.107269 0.107269 |
1 0.418145 0.659094 0.084591 0.084591 |
0 0.493161 0.276681 0.110234 0.110234 |
1 0.436496 0.147033 0.110481 0.110481 |
0 0.239877 0.383125 0.118972 0.118972 |
0 0.345871 0.213761 0.126606 0.126606 |
0 0.378440 0.133567 0.089045 0.089045 |
0 0.497308 0.194378 0.060473 0.060473 |
0 0.284404 0.723853 0.108257 0.108257 |
0 0.514361 0.205948 0.074402 0.074402 |
0 0.420338 0.477214 0.112791 0.112791 |
0 0.340937 0.267908 0.078365 0.078365 |
0 0.562156 0.281955 0.042370 0.042370 |
1 0.137725 0.409390 0.106487 0.106487 |
1 0.243211 0.436680 0.123409 0.123409 |
1 0.146968 0.357183 0.107180 0.107180 |
0 0.272112 0.298127 0.121748 0.121748 |
End of preview.
YAML Metadata Warning:empty or missing yaml metadata in repo card
Check out the documentation for more information.
Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection
by Sumin Seo, In Kyu Lee, Hyun-Woo Kim, Jaesik Min, and Chung-Hwan Jung
News
📣 2025/10/27: Our relabeled dataset and final manuscript are now available online.
📣 Accepted at MICCAI 2025! Check out our arXiv paper, and the dataset will be updated soon.
Introducing DiGDA
We present DiGDA, a novel data augmentation method for lesion detection that generates realistic angiography images with varying levels of lesion severity by conditioning a diffusion model on lesion-specific vessel masks.
Our pipeline produces high-quality synthetic images that preserve anatomical vessel structures while reflecting user-specified lesion severity, including highly severe cases.
We demonstrate that incorporating these user-guided synthetic images into lesion detection pipelines significantly improves detection performance.
As part of our contribution, we release the updated annotations of the ARCADE dataset, specifically re-annotated for stenosis severity classification and lesion detection tasks.
How to Use Our Dataset
- Download images from ARCADE dataset and place them under the corresponding directory:
data/{split}/images. - Check out our annotations:
- Each annotation is provided in YOLO format:
class x_center y_center width height. All coordinates are normalized by the original image size. - Class information:
- Class 0: Non-severe lesion (%DS ≥ 50% and %DS < 70%) evaluated by our in-house experienced clinicians.
- Class 1: Severe lesion (%DS ≥ 70%).
- Train or evaulate your model using our relabeled dataset.
- Share your experience with us!
- We welcome feedback, suggestions, and collaboration.
- Feel free to contact the authors (sumin.seo@medipixel.io).
Citation and License
If you use our relabeled dataset, please cite both our paper and the ARCADE dataset paper, and follow the CC BY-NC-SA 4.0 license.
@inproceedings{seo2025diffusion,
author = {Seo, Sumin and Lee, In Kyu and Kim, Hyun-Woo and Min, Jaesik and Jung, Chung-Hwan},
title = {Diffusion-Based User-Guided Data Augmentation for Coronary Stenosis Detection},
booktitle = {International Conference on Medical Image Computing and Computer-Assisted Intervention},
year = {2025},
publisher = {Springer},
note = {DOI will be available upon publication}
}
@article{popov2024dataset,
author = {Popov, Maxim and Amanturdieva, Akmaral and Zhaksylyk, Nuren and Alkanov, Alsabir and Saniyazbekov, Adilbek and Aimyshev, Temirgali and Ismailov, Eldar and Bulegenov, Ablay and Kuzhukeyev, Arystan and Kulanbayeva, Aizhan and others},
title = {Dataset for Automatic Region-Based Coronary Artery Disease Diagnostics Using X-ray Angiography Images},
journal = {Scientific Data},
volume = {11},
number = {1},
pages = {20},
year = {2024},
publisher = {Nature Publishing Group UK, London}
}
📧 Contact Info
If you have any questions, please contact the corresponding author, Sumin Seo (sumin.seo@medipixel.io).
- Downloads last month
- 4,708