OctoMed/TissueMNIST

---
dataset_info:
features:
- name: image
dtype: image
- name: image_hash
dtype: string
- name: question
dtype: string
- name: options
sequence: string
- name: answer
dtype: string
- name: responses
sequence: string
splits:
- name: train
- name: test
configs:
- config_name: default
data_files:
- split: train
path: data/train-*
- split: test
path: data/test-*
---

# TissueMNIST - Human Kidney Cortex Cell Classification

## Description
This dataset contains gray-scale microscope images of human kidney cortex cells for cell-type classification. The task involves classifying each cell into one of eight categories based on morphological features. 16 reasoning traces were collected for each example in this task by sampling with GPT-4o, available in the `responses` column. We greatly appreciate and build from the original data source available at https://medmnist.com

## Data Fields
- `question`: The classification question about kidney cortex cell type
- `options`: Multiple choice options representing different kidney cortex cell categories
- `answer`: The correct cell category
- `image`: Gray-scale image of a human kidney cortex cell
- `responses`: Model reasoning responses (in train split)

## Splits
- `train`: Training data with model responses
- `test`: Test data for evaluation

## Usage
```python
from datasets import load_dataset

dataset = load_dataset("OctoMed/TissueMNIST")
```

## Citation

If you find our work helpful, feel free to give us a cite!

```
@article{ossowski2025octomed,
title={OctoMed: Data Recipes for State-of-the-Art Multimodal Medical Reasoning},
author={Ossowski, Timothy and Zhang, Sheng and Liu, Qianchu and Qin, Guanghui and Tan, Reuben and Naumann, Tristan and Hu, Junjie and Poon, Hoifung},
journal={arXiv preprint arXiv:2511.23269},
year={2025}
}
```