---
license: cc0-1.0
task_categories:
- image-segmentation
tags:
- medical
- dermoscopy
- skin
- melanoma
- lesion-segmentation
pretty_name: ISIC 2017 — Skin Lesion Segmentation (Task 1)
size_categories:
- 1K<n<10K
dataset_info:
  features:
  - name: image_id
    dtype: string
  - name: image
    dtype: image
  - name: mask
    dtype: image
  - name: age_approximate
    dtype: float32
  - name: sex
    dtype: string
  - name: in_isic2018_train
    dtype: bool
  splits:
  - name: train
    num_bytes: 5802730760
    num_examples: 2000
  - name: validation
    num_bytes: 871819732
    num_examples: 150
  - name: test
    num_bytes: 5575275700
    num_examples: 600
  download_size: 12250441569
  dataset_size: 12249826192
configs:
- config_name: default
  data_files:
  - split: train
    path: data/train-*
  - split: validation
    path: data/validation-*
  - split: test
    path: data/test-*
---

# ISIC 2017 — Skin Lesion Segmentation (Task 1)

Dermoscopic RGB images of skin lesions (melanoma / seborrheic keratosis / nevus)
with expert binary lesion-boundary masks, from the ISBI 2017 challenge
*"Skin Lesion Analysis Toward Melanoma Detection"* hosted by the International
Skin Imaging Collaboration (ISIC).

| Split | Images | Masks | Melanoma | Seborrheic keratosis | Nevus |
|---|---|---|---|---|---|
| train | 2000 | 2000 | 374 | 254 | 1372 |
| validation | 150 | 150 | 30 | 42 | 78 |
| test | 600 | 600 | 117 | 90 | 393 |

All three splits carry public ground truth. The test split is the official
**`Test_v2`** release — the final public revision of the challenge test set
(v1 was withdrawn upstream). Resolutions vary widely (~540×722 up to
4499×6748 px).

## Schema

| Column | Type | Notes |
|---|---|---|
| `image_id` | string | Stable ISIC Archive ID (`ISIC_XXXXXXX`) — cross-references all ISIC challenge editions |
| `image` | Image | Original challenge JPEG, bytes unmodified |
| `mask` | Image | Original expert mask PNG: **0 = background, 255 = lesion** |
| `age_approximate` | float | From the official challenge metadata CSV; null when unknown |
| `sex` | string | `male` / `female`; null when unknown |
| `in_isic2018_train` | bool | True if this image also appears in ISIC 2018 Task 1's training split (see below) |

Masks were created by expert clinicians via manual polyline tracing or a
supervised flood-fill workflow (one published mask per image; the per-image
method is not disclosed). The `*_superpixels.png` scaffolding files and the
Part-2 (dermoscopic features) / Part-3 (classification) ground truth belong to
other challenge tasks and are not mirrored here.

## ⚠️ Overlap with ISIC 2018 (leakage warning)

ISIC challenge editions draw from the same growing archive. Measured by exact
`image_id` intersection against ISIC 2018 Task 1:

- **2,450 / 2,750 images (89.1%) of ISIC 2017 reappear in ISIC 2018's training
  split** — train 1800/2000, validation 121/150, test 529/600.
- ISIC 2018's own validation/test splits share **zero** IDs with ISIC 2017.
- Consequence: a model trained on ISIC 2018 Task 1 training data has already
  seen 650 of the 750 ISIC 2017 validation+test images. Filter on
  `in_isic2018_train` (or use `isic_2017_2018_split_ids.json` at the repo root)
  before treating the two datasets as independent benchmarks.
- Filename-level matching is a **lower bound**: pixel-level near-duplicates
  (rescaled/re-encoded variants) exist across ISIC editions and against ISIC
  2016 — see Cassidy et al., *Medical Image Analysis* 75:102305 (2022),
  https://github.com/mmu-dermatology-research/isic_duplicate_removal_strategy.

## Provenance

Official author-hosted S3 bucket
(`https://isic-archive.s3.amazonaws.com/challenges/2017/`), zips verified
byte-exact against Content-Length; image/mask counts match the challenge paper
(2000/150/600). Original encoded bytes are embedded unmodified.

## License

**CC-0 (public domain)**, per the 2017 section of the
[ISIC Challenge data page](https://challenge.isic-archive.com/data/).

## Citation

```bibtex
@inproceedings{codella2018skin,
  title     = {Skin lesion analysis toward melanoma detection: A challenge at
               the 2017 International Symposium on Biomedical Imaging (ISBI),
               hosted by the International Skin Imaging Collaboration (ISIC)},
  author    = {Codella, Noel C. F. and Gutman, David and Celebi, M. Emre and
               Helba, Brian and Marchetti, Michael A. and Dusza, Stephen W. and
               Kalloo, Aadi and Liopyris, Konstantinos and Mishra, Nabin and
               Kittler, Harald and Halpern, Allan},
  booktitle = {2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI)},
  pages     = {168--172},
  year      = {2018},
  doi       = {10.1109/ISBI.2018.8363547}
}
```