Datasets:

dunktra
/

dermacheck-temporal-pairs

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

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

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)

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)