Angelou0516/BKAI-IGH_NeoPolyp

BKAI-IGH NeoPolyp (binary, MedCLIPSeg mirror)

Re-hosted mirror of the BKAI-IGH NeoPolyp "Small" subset (Lan et al., 2021), originally released through the BKAI-IGH NeoPolyp Kaggle competition, intended for use with EasyMedSeg.

This mirror is rebuilt from the TahaKoleilat/MedCLIPSeg HF dataset's BKAI.zip (the only freely-fetchable HF rehost we found that ships the masks). We chose this source because the canonical Kaggle URL requires Kaggle competition-acceptance + an API token, which is awkward for downstream automation.

Composition

Split	Images	With polyp
train	799	(computed)
val	100	(computed)
test	100	(computed)
All	999

Image dimensions: variable (~1280 × 950–1000 px), heterogeneous endoscopy frames in JPEG. The original Kaggle release contains 1,000 train (with masks) + 200 test (held-out masks); this mirror uses the 1,000 train pool re-split into 799/100/100. The 200-image canonical Kaggle test split with no public masks is not included.

Mask caveat — binary only

The upstream MedCLIPSeg mirror saved the original 3-channel RGB-coded semantic masks as JPEG-compressed grayscale. JPEG compression introduces boundary noise (we observed pixel values 1–40 and 211–254 in addition to 0/255), and JPEG-on-label-map is inherently lossy.

This mirror thresholds at > 127 to recover a clean binary {0, 255} mask. The 3-class (background / non-neoplastic polyp / neoplastic polyp) distinction in the original Kaggle PNGs is NOT recoverable from this source. Use this mirror for binary polyp segmentation only. Pull the canonical Kaggle data directly if you need the 3-class formulation required to reproduce the NeoUNet / BlazeNeo benchmarks.

Schema

Column	Type	Description
image	Image	Source RGB frame (PNG bytes, variable size)
mask	Image	Binary mask (L mode, 0/255)
image_id	string	32-char hex stem from the source filename
split	string	train / val / test
has_polyp	bool	True iff the mask contains any foreground

License

The original Kaggle release does not declare a public license; usage is implicitly governed by Kaggle competition rules ("research / academic use"). The intermediate TahaKoleilat/MedCLIPSeg mirror redistributes under CC-BY-NC-4.0 (mirror-imposed, not author-confirmed). Treat as research / non-commercial only until BKAI/IGH publishes a formal license.

Citation

@inproceedings{lan2021neounet,
  title     = {{NeoUNet}: Towards Accurate Colon Polyp Segmentation
               and Neoplasm Detection},
  author    = {Lan, Phan Ngoc and An, Nguyen Sy and Hang, Dao Viet
               and Long, Dao Van and Trung, Tran Quang
               and Thuy, Nguyen Thi and Sang, Dinh Viet},
  booktitle = {Advances in Visual Computing -- ISVC 2021},
  series    = {Lecture Notes in Computer Science},
  volume    = {13018},
  pages     = {15--28},
  publisher = {Springer},
  year      = {2021},
  doi       = {10.1007/978-3-030-90436-4_2}
}

@article{an2022blazeneo,
  title   = {{BlazeNeo}: Blazing Fast Polyp Segmentation
             and Neoplasm Detection},
  author  = {An, Nguyen Sy and Lan, Phan Ngoc and Hang, Dao Viet
             and Long, Dao Van and Trung, Tran Quang
             and Thuy, Nguyen Thi and Sang, Dinh Viet},
  journal = {IEEE Access},
  volume  = {10},
  pages   = {43669--43684},
  year    = {2022},
  doi     = {10.1109/ACCESS.2022.3168693}
}