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DermaLens Skin Cancer Dataset
This dataset repo documents the data pipeline used to train the DermaLens V3 skin cancer classification model.
Source Dataset
HAM10000 (Human Against Machine with 10000 training images) — accessed via marmal88/skin_cancer on HuggingFace.
from datasets import load_dataset
ds = load_dataset("marmal88/skin_cancer")
Dataset Statistics
| Split | Images | Malignant | Benign | Positive Rate |
|---|---|---|---|---|
| Train | 10,683 | ~2,093 | ~8,590 | 19.6% |
| Validation | 1,335 | ~263 | ~1,072 | 19.7% |
| Test | 1,336 | ~253 | ~1,083 | 18.9% |
| Total | 13,354 | ~2,609 | ~10,745 | 19.5% |
Label Mapping (Binary)
Original Class (dx) |
Binary Label | Category |
|---|---|---|
melanoma |
1 (Malignant) | Malignant melanocytic |
basal_cell_carcinoma |
1 (Malignant) | Non-melanocytic malignant |
actinic_keratoses |
1 (Malignant) | Pre-cancerous |
melanocytic_Nevi |
0 (Benign) | Common moles |
benign_keratosis-like_lesions |
0 (Benign) | Seborrheic keratoses etc. |
dermatofibroma |
0 (Benign) | Benign fibrous |
vascular_lesions |
0 (Benign) | Angiomas etc. |
MALIGNANT_CLASSES = {"melanoma", "basal_cell_carcinoma", "actinic_keratoses"}
label = 1 if item["dx"] in MALIGNANT_CLASSES else 0
Preprocessing
- Resize: 384x384 pixels
- Normalization: ImageNet stats (mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])
- Augmentation (train only): RandomResizedCrop, Flips, Rotation, ColorJitter, CoarseDropout, CLAHE
- Oversampling: Malignant samples repeated 3x
Model Performance (DermaLens V3)
| Metric | Value |
|---|---|
| Test ROC-AUC | 0.9753 |
| Test PR-AUC | 0.9127 |
| Test F1 | 0.8457 |
| Sensitivity | 94% |
| Specificity | 91% |
Model weights: dheraingoud/dermalens-model
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