| --- |
| license: cc0-1.0 |
| task_categories: |
| - image-segmentation |
| tags: |
| - medical |
| - dermoscopy |
| - skin-lesion |
| - melanoma |
| - lesion-segmentation |
| - isic |
| pretty_name: ISIC 2016 (ISBI 2016) — Part 1 lesion segmentation |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # ISIC 2016 — Part 1: Lesion Segmentation |
|
|
| **1,279 dermoscopy images** (900 train / 379 test) of pigmented skin lesions |
| with **expert binary lesion-boundary masks**, from the ISBI 2016 challenge |
| *"Skin Lesion Analysis toward Melanoma Detection"* hosted by the International |
| Skin Imaging Collaboration (ISIC). A snapshot of the ISIC Archive. |
|
|
| - **Modality:** dermoscopy (2D RGB JPEG), variable 0.5–12 MP resolution |
| - **Organ:** skin (pigmented lesions — melanoma vs benign) |
| - **Ground truth:** one binary mask per image (PNG, `0`=background, |
| `255`=lesion), traced by an expert clinician via a semi-automated |
| (seed + flood-fill) or manual (polyline) process. Single annotation tier — |
| every image has exactly one mask. |
| - **Expert ceiling:** pairwise inter-observer Jaccard on 100 images of this |
| data is **≈ 0.786** (Codella et al., IBM J. Res. Dev. 2017; see |
| arXiv:1902.03368 §2.1) — treat scores near that as expert-level. |
|
|
| ## Scope |
|
|
| The ISBI 2016 challenge had five parts. This repository ships **Part 1 |
| (lesion segmentation) only.** Not included: Part 2 superpixel + dermoscopic- |
| feature classification JSONs, Part 2B dermoscopic-feature masks (globules / |
| streaks on an 807-image subset — *not* lesion segmentation ground truth), and |
| Part 3/3B malignancy classification labels. |
|
|
| ## Schema |
|
|
| | column | type | contents | |
| |---|---|---| |
| | `image_id` | string | ISIC Archive id, e.g. `ISIC_0000000` — the cross-challenge join key | |
| | `image` | Image | original challenge JPEG, unmodified | |
| | `mask` | Image | original challenge PNG, single-channel, values `{0, 255}` | |
| | `in_isic2018_train` | bool | `True` iff this image is also in ISIC 2018 Task 1 **training** ground truth | |
|
|
| Splits: `train` (900 rows), `test` (379 rows). No validation split was released |
| for this challenge. |
|
|
| ## ⚠️ Overlap with ISIC 2018 (leakage warning) |
|
|
| The ISIC 2016/2017/2018 challenge datasets are successive snapshots of the same |
| archive and share the `ISIC_<7-digit>` id namespace. Measured against the |
| official ISIC 2018 Task 1 training ground truth (2,594 ids): |
|
|
| | intersection | count | |
| |---|---| |
| | ISIC 2016 **train** ∩ ISIC 2018 train | **806 / 900** (89.6%) | |
| | ISIC 2016 **test** ∩ ISIC 2018 train | **339 / 379** (89.4%) | |
| | ISIC 2016 (any) ∩ ISIC 2018 **val/test** | **0** | |
|
|
| Consequences: |
|
|
| - Any model **fine-tuned on ISIC 2018 training data is contaminated** for |
| evaluation on ISIC 2016 (both splits). Filter with the |
| `in_isic2018_train` column: |
| `ds.filter(lambda r: not r["in_isic2018_train"])`. |
| - Zero-shot evaluation on both challenges' *eval* sets never scores the same |
| image twice (2016's test set is disjoint from 2018's val/test). |
| - For the 1,145 shared images the 2018 masks were slightly **revised** |
| (IoU 0.988–0.997 vs the 2016 masks) — do not mix the two years as |
| interchangeable label sources. |
|
|
| ## Provenance |
|
|
| Built from the four official challenge zips (no registration required), |
| fetched byte-exact against their Content-Length and matching the challenge |
| paper's counts (900 train / 379 test) exactly: |
|
|
| ``` |
| https://isic-challenge-data.s3.amazonaws.com/2016/ISBI2016_ISIC_Part1_Training_Data.zip |
| https://isic-challenge-data.s3.amazonaws.com/2016/ISBI2016_ISIC_Part1_Training_GroundTruth.zip |
| https://isic-challenge-data.s3.amazonaws.com/2016/ISBI2016_ISIC_Part1_Test_Data.zip |
| https://isic-challenge-data.s3.amazonaws.com/2016/ISBI2016_ISIC_Part1_Test_GroundTruth.zip |
| ``` |
|
|
| Images and masks are byte-identical to the originals (no re-encoding, no |
| resizing). Every mask was verified single-channel with values ⊆ {0, 255}, |
| non-empty, and pixel-dimension-identical to its image. |
|
|
| ## Usage |
|
|
| ```python |
| from datasets import load_dataset |
| |
| ds = load_dataset("MedOtter/ISIC2016") # train / test |
| sample = ds["train"][0] |
| image = sample["image"] # PIL RGB |
| mask = sample["mask"] # PIL L, {0, 255} |
| binary = mask.point(lambda p: p > 0) # -> {0, 1} |
| |
| # leakage-safe subset w.r.t. models trained on ISIC 2018: |
| clean_test = ds["test"].filter(lambda r: not r["in_isic2018_train"]) # 40 rows |
| ``` |
|
|
| ## License |
|
|
| **CC0 1.0 (public domain)** — as stated for the 2016 challenge on the |
| [ISIC challenge data page](https://challenge.isic-archive.com/data/). |
| Attribution is requested: cite the challenge paper below. |
|
|
| ## Citation |
|
|
| ```bibtex |
| @article{gutman2016skin, |
| title = {Skin Lesion Analysis toward Melanoma Detection: A Challenge at |
| the International Symposium on Biomedical Imaging (ISBI) 2016, |
| hosted by the International Skin Imaging Collaboration (ISIC)}, |
| author = {Gutman, David and Codella, Noel C. F. and Celebi, Emre and |
| Helba, Brian and Marchetti, Michael and Mishra, Nabin and |
| Halpern, Allan}, |
| journal = {arXiv preprint arXiv:1605.01397}, |
| year = {2016} |
| } |
| ``` |
|
|
| ## Related |
|
|
| - [`MedOtter/ISIC2018`](https://huggingface.co/datasets/MedOtter/ISIC2018) — |
| ISIC 2018 Task 1 (2,594 / 100 / 1,000). See the overlap table above before |
| using both. |
|
|
