Datasets:
metadata
license: cc
task_categories:
- image-classification
- image-segmentation
language:
- en
tags:
- ISIC
- HAM10000
- dermatology
- medical
- skin-disease
- bbox
- spatial-annotations
size_categories:
- 1K<n<10K
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
dataset_info:
features:
- name: image
dtype: image
- name: lesion_id
dtype: string
- name: image_id
dtype: string
- name: diagnosis
dtype: string
- name: dx_type
dtype: string
- name: age
dtype: float32
- name: sex
dtype: string
- name: localization
dtype: string
- name: bbox
sequence: float32
- name: area_coverage
dtype: float32
splits:
- name: train
num_bytes: 3004910314.4
num_examples: 8012
- name: test
num_bytes: 751227578.6
num_examples: 2003
download_size: 3755966729
dataset_size: 3756137893
HAM10000 with Spatial Annotations and Bounding Box Coordinates
Enhanced version of HAM10000 dataset with bounding box coordinates and spatial descriptions for skin lesion localization.
Dataset Description
This dataset extends the original HAM10000 dermatology dataset with:
- Bounding box coordinates for lesion localization
- Spatial descriptions (e.g., "located in center-center region")
- Area coverage statistics
- Mask availability flags
Features
- image: RGB skin lesion images
- diagnosis: Skin condition diagnosis codes (mel, nv, bkl, etc.)
- bbox: [x1, y1, x2, y2] bounding box coordinates
- spatial_description: Natural language location descriptions
- area_coverage: Lesion area relative to image size
- localization: Body part location
- age/sex: Patient demographics
Usage
from datasets import load_dataset
dataset = load_dataset("abaryan/ham10000_bbox")
train_data = dataset["train"]
# Access image and annotations
sample = train_data[0]
image = sample["image"]
bbox = sample["bbox"]
description = sample["spatial_description"]
Citation
Based on original HAM10000 dataset. Enhanced with spatial annotations for vision-language model training. """