# A Curated and Re-annotated Peripheral Blood Cell Dataset Integrating Four Public Resources

arXiv: https://arxiv.org/abs/2407.13214 · HTML: https://mlsanity.com/paper/2407.13214/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 4 of its 25 references that are in the map.
- First posted on arXiv 18 Jul 2024.

## Builds on (most cited first, 4 of 4)

- [Faster R-CNN: Towards Real-Time Object Detection with Region Proposal Networks](https://mlsanity.com/paper/1506.01497/) (2015)
- [SSD: Single Shot MultiBox Detector](https://mlsanity.com/paper/1512.02325/) (2015)
- [Precision Health Data: Requirements, Challenges and Existing Techniques for Data Security and Privacy](https://mlsanity.com/paper/2008.10733/) (2020)
- [A convnet for non-maximum suppression](https://mlsanity.com/paper/1511.06437/) (2015)

## Built on by (most cited first, 3 of 3)

- [Generalizable Blood Cell Detection via Unified Dataset and Faster R-CNN](https://mlsanity.com/paper/2511.08465/) (2025)
- [Exemplar Diffusion: Improving Medical Object Detection with Opportunistic Labels](https://mlsanity.com/paper/2603.15267/) (2026)
- [One Tile, Multiple Instances: Rethinking MIL for Sparse Diagnostic Evidence](https://mlsanity.com/paper/2610.04853/) (2026)

## Abstract (arXiv)

We present TXL-PBC, a curated and re-annotated peripheral blood cell dataset constructed by integrating four publicly available resources: Blood Cell Count and Detection (BCCD), Blood Cell Detection Dataset (BCDD), Peripheral Blood Cells (PBC), and Raabin White Blood Cell (Raabin-WBC). Through rigorous sample selection, semi-automatic annotation using the YOLOv8n model, and comprehensive manual review, we ensured high annotation accuracy and consistency. The final dataset contains 1,260 images and 18,143 bounding box annotations for three major blood cell types: white blood cells (WBC), red blood cells (RBC), and platelets. We provide detailed visual analyses of the data distribution, demonstrating the diversity and balance of the dataset. To further validate the quality and utility of TXL-PBC, we trained several mainstream object detection models, including YOLOv5s, YOLOv8s, YOLOv11s, SSD300, Faster R-CNN, and RetinaNet, and report their baseline performance. The TXL-PBC dataset is openly available on Figshare and GitHub, offering a valuable resource for the development and benchmarking of blood cell detection models and related machine learning research.