Datasets:
CVC-ClinicDB (file mirror)
612 frames extracted from 29 colonoscopy sequences, each with a pixel-level polyp mask (Bernal et al., 2015). Native resolution 384x288.
This is a plain file mirror, not a datasets-format repo: images and masks
are stored as files so any path-based dataloader can consume them directly
after snapshot_download. The dataset viewer is disabled for that reason.
Layout
CVC-ClinicDB/
Original/*.png # 612 RGB frames, 384x288
Ground Truth/*.png # 612 binary masks, same stem as their image
class_dict.csv # background=(0,0,0), polyp=(255,255,255)
metadata.csv # frame_id, sequence_id, image_path, mask_path
metadata.csv carries the sequence_id column: the 612 frames come from
only 29 sequences, so a frame-level random split leaks near-duplicate frames
across train and validation. Group by sequence_id for an honest split.
Its path columns were rewritten to match this repo's layout.
Usage
from huggingface_hub import snapshot_download
snapshot_download(
repo_id="berkaytrhn/cvc-clinicdb",
repo_type="dataset",
local_dir="data/cvc-clinicdb",
allow_patterns=["CVC-ClinicDB/**", "*.csv"], # skip the TIF tree
)
# -> data/cvc-clinicdb/CVC-ClinicDB/Original, .../Ground Truth
Source and citation
The official distribution is a .rar behind a JavaScript-rendered Drive link on
https://polyp.grand-challenge.org/CVCClinicDB/ ; the PNG rendering here follows
the widely used Kaggle mirror balraj98/cvcclinicdb. Released for research use.
Cite the original paper, not this mirror:
@article{bernal2015wm,
title = {WM-DOVA maps for accurate polyp highlighting in colonoscopy:
Validation vs. saliency maps from physicians},
author = {Bernal, Jorge and Sanchez, F. Javier and
Fernandez-Esparrach, Gloria and Gil, Debora and
Rodriguez, Cristina and Vilarino, Fernando},
journal = {Computerized Medical Imaging and Graphics},
volume = {43},
pages = {99--111},
year = {2015},
publisher = {Elsevier}
}
- Downloads last month
- 901