| --- |
| dataset_info: |
| features: |
| - name: image_before |
| dtype: image |
| - name: image_after |
| dtype: image |
| - name: text_prompt |
| dtype: string |
| - name: label |
| dtype: int32 |
| - name: change_description |
| dtype: string |
| - name: method |
| dtype: string |
| - name: pair_id |
| dtype: string |
| splits: |
| - name: train |
| num_bytes: 365953410 |
| num_examples: 630 |
| - name: validation |
| num_bytes: 78418588 |
| num_examples: 135 |
| - name: test |
| num_bytes: 78418588 |
| num_examples: 135 |
| download_size: 522486493 |
| dataset_size: 522790586 |
| configs: |
| - config_name: default |
| data_files: |
| - split: train |
| path: data/train-* |
| - split: validation |
| path: data/validation-* |
| - split: test |
| path: data/test-* |
| license: cc-by-nc-sa-4.0 |
| task_categories: |
| - image-classification |
| - image-text-to-text |
| - visual-question-answering |
| language: |
| - en |
| tags: |
| - medical |
| - dermatology |
| - melanoma |
| - skin-cancer |
| - temporal-change-detection |
| - synthetic-data |
| - medical-imaging |
| - multimodal |
| - medgemma |
| pretty_name: 'DermaCheck: Temporal Dermatoscopic Image Pairs' |
| size_categories: |
| - 1K<n<10K |
| --- |
| |
| # DermaCheck Temporal Pairs Dataset |
|
|
| ## Dataset Description |
|
|
| Synthetic temporal image pairs for training MedGemma to detect changes in dermatoscopic images over time. |
|
|
| **Created for:** MedGemma Impact Challenge 2026 - Novel Task Prize (temporal change detection) |
|
|
| ## Dataset Statistics |
|
|
| - **Total pairs:** 900 |
| - **Train:** 630 pairs (70.0%) |
| - **Validation:** 135 pairs (15.0%) |
| - **Test:** 135 pairs (15.0%) |
|
|
| ## Generation Methods |
|
|
| 1. **Controlled Augmentation** (~50%): Original HAM10000 images augmented to simulate temporal evolution |
| - Size increase: 10-30% |
| - Border irregularity: Elastic transforms |
| - Color variation: HSV adjustments |
| - Based on clinical research: melanoma evolution patterns |
|
|
| 2. **Natural Pairing** (~50%): Similar lesions from HAM10000 matched as temporal proxies |
| - Feature-based similarity matching |
| - Cosine similarity range: [0.6, 0.85] |
| - Same diagnosis category (melanoma focus) |
|
|
| 3. **Unchanged Pairs** (~15%): Negative examples for balanced training |
| - Same image duplicated as before/after |
| - Label: 0 (no change) |
|
|
| ## Dataset Structure |
|
|
| Each example contains: |
| - `image_before`: PIL Image (before timepoint) |
| - `image_after`: PIL Image (after timepoint) |
| - `text_prompt`: ABCDE-focused change detection question |
| - `label`: 0 (no change) or 1 (change detected) |
| - `change_description`: Educational explanation of changes |
| - `method`: Generation method (controlled_augmentation, natural_pairing, unchanged) |
| - `pair_id`: Unique identifier |
|
|
| ## Source Data |
|
|
| Based on HAM10000 dataset: |
| - Tschandl, P., Rosendahl, C. & Kittler, H. The HAM10000 dataset. *Sci Data* 5, 180161 (2018). |
| - License: CC BY-NC-SA 4.0 (Non-commercial use) |
| - URL: https://www.kaggle.com/datasets/kmader/skin-cancer-mnist-ham10000 |
|
|
| ## Intended Use |
|
|
| - Fine-tuning MedGemma for temporal change detection |
| - Educational tool development |
| - Research purposes only (not for clinical diagnosis) |
|
|
| ## Citation |
|
|
| If you use this dataset, please cite both the original HAM10000 dataset and this derived work. |
|
|
| ## License |
|
|
| CC BY-NC-SA 4.0 (Non-commercial use, per HAM10000 license) |
