# A large expert-annotated single-cell peripheral blood dataset for hematological disease diagnostics

## TL;DR

This paper introduces a large, expert-annotated dataset of peripheral blood cells to help improve AI-based diagnosis of blood diseases.

## Contribution

The paper provides a publicly available dataset of over 40,000 annotated single-cell images for hematological disease diagnostics.

## Key findings

- The dataset includes 18 cell classes annotated by cytomorphology experts.
- It is intended to support the development of reliable diagnostic tools using machine learning.
- The dataset is publicly accessible for medical and AI research.

## Abstract

Distinguishing cell types in a peripheral blood smear is critical for diagnosing blood diseases, such as leukemia subtypes. Artificial intelligence can assist in automating cell classification. For training robust machine learning algorithms, however, large and well-annotated single-cell datasets are pivotal. Here, we introduce a large, publicly available, annotated peripheral blood dataset comprising >40,000 single-cell images classified into 18 classes by cytomorphology experts from the Munich Leukemia Laboratory, the largest European laboratory for blood disease diagnostics. By making our dataset publicly available, we provide a valuable resource for medical and machine learning researchers and support the development of reliable and clinically relevant diagnostic tools for diagnosing hematological diseases.