dermatology
SCIN-Dermatology-Raw-Images
SCIN-Dermatology-Raw-Images
This dataset contains 6,517 patient-submitted photographs organized into 3,061 clinical cases of common skin diseases. The source images are curated from the public Google Skin Condition Image Network (SCIN) corpus, cleansed of quality and gradability conflicts, and paired with complete patient-reported demographics, clinical symptoms, and dermatologist gradings.
Dataset Structure
This repository follows the standard Hugging Face… See the full description on the dataset page: https://huggingface.co/datasets/HawkFranklin-Research/SCIN-Dermatology-Raw-Images.Dermatology_40k
Dermatology VQA Natural All
This dataset contains all currently available dermatology diagnosis VQA records
from ISIC 2019 and MILK10k after dropping unknown/other labels.
Each row follows the multimodal schema:
conversations: two-turn human/GPT conversation
problem: ABCD multiple-choice question
answer: correct option in A. label format
images: one image path in a list
qid: stable question id in {data_source}/{image_id} format
The uploaded Hub dataset is stored as parquet… See the full description on the dataset page: https://huggingface.co/datasets/CAIR-M3LLM/Dermatology_40k.dermatology-dataset-acne-redness-and-bags-under-the-eyes
Skin Defects Dataset
The dataset contains images of individuals with various skin conditions: acne, skin redness, and bags under the eyes. Each person is represented by 3 images showcasing their specific skin issue. The dataset encompasses diverse demographics, age, ethnicities, and genders.
The dataset is created on the basis of Facial Skin Condition Dataset
Types of defects in the dataset: acne, skin redness & bags under the eyes
Acne photos: display different… See the full description on the dataset page: https://huggingface.co/datasets/UniqueData/dermatology-dataset-acne-redness-and-bags-under-the-eyes.CleanPatrick
CleanPatrick: A Benchmark for Data Cleaning
Welcome to CleanPatrick, the first large-scale benchmark designed for data cleaning in the image domain.
Built on the Fitzpatrick17k dermatology dataset, CleanPatrick is a dataset for measuring the performance in detecting three major data quality issues:
off-topic samples, near-duplicates, and label errors.
Overview
CleanPatrick consists of dermatological images annotated with over 500,000 binary labels across three data… See the full description on the dataset page: https://huggingface.co/datasets/Digital-Dermatology/CleanPatrick.Dermatology-Question-Answer-Dataset-For-Fine-Tuning
Dataset Details
The data set has about 1 Million Tokens for Training and about 1500 question answers.
Dataset Description
This dataset is a comprehensive compilation of questions related to dermatology, spanning inquiries about various skin diseases, their symptoms, recommended medications, and available treatment modalities. Each question is paired with a concise and informative response, making it an ideal resource for training and fine-tuning language models in the… See the full description on the dataset page: https://huggingface.co/datasets/Mreeb/Dermatology-Question-Answer-Dataset-For-Fine-Tuning.SCIN-Dermatology-Gemma4-VQA
SCIN-Dermatology-Gemma4-VQA
This dataset contains conversational visual question answering (VQA) dialogues structured specifically for fine-tuning on-device multimodal Vision-Language Models (VLMs), such as Gemma 4 E4B Vision/Audio.
The dataset is compiled from the Skin Condition Image Network (SCIN) cohort, cleansing conflicts and joining raw patient-reported demographics, symptoms, and Fitzpatrick/Monk skin tones.
Dataset Structure
The dataset contains two… See the full description on the dataset page: https://huggingface.co/datasets/HawkFranklin-Research/SCIN-Dermatology-Gemma4-VQA.