Datasetpublic

dheraingoud/dermalens-datasets

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%… See the full description on the dataset page: https://huggingface.co/datasets/dheraingoud/dermalens-datasets.

sourceHugging Facecc-by-nc-sa-4.0updated 7mo agoView on Hugging Face

0likes70downloads

Dataset Card

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.

python

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.

python

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`