---
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: 3756137893.0
---

# 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

```python
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.
"""