| --- |
| license: other |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - endoscopy |
| - colonoscopy |
| - polyp |
| - segmentation |
| - bkai |
| size_categories: |
| - n<10K |
| --- |
| |
| # 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](https://www.kaggle.com/c/bkai-igh-neopolyp/), |
| intended for use with [EasyMedSeg](https://github.com/). |
|
|
| This mirror is rebuilt from the |
| [`TahaKoleilat/MedCLIPSeg`](https://huggingface.co/datasets/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 |
|
|
| ```bibtex |
| @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} |
| } |
| ``` |
|
|
