Datasets:
File size: 2,161 Bytes
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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.
""" |