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

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):

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

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. Attribution is requested: cite the challenge paper below.

Citation

@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 — ISIC 2018 Task 1 (2,594 / 100 / 1,000). See the overlap table above before using both.