| --- |
| 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| (v1 was withdrawn upstream).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} |
| } |
| ``` |
| |
