# Benchmarking White Blood Cell Classification Under Domain Shift

arXiv: https://arxiv.org/abs/2303.01777 · HTML: https://mlsanity.com/paper/2303.01777/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- Builds on 8 of its 34 references that are in the map.
- First posted on arXiv 3 Mar 2023.

## Builds on (most cited first, 8 of 8)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [An Image is Worth 16x16 Words: Transformers for Image Recognition at Scale](https://mlsanity.com/paper/2010.11929/) (2020)
- [Very Deep Convolutional Networks for Large-Scale Image Recognition](https://mlsanity.com/paper/1409.1556/) (2014)
- [Batch Normalization: Accelerating Deep Network Training by Reducing Internal Covariate Shift](https://mlsanity.com/paper/1502.03167/) (2015)
- [A ConvNet for the 2020s](https://mlsanity.com/paper/2201.03545/) (2022)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [A Closer Look at Deep Learning Heuristics: Learning rate restarts, Warmup and Distillation](https://mlsanity.com/paper/1810.13243/) (2018)
- [Rethinking "Batch" in BatchNorm](https://mlsanity.com/paper/2105.07576/) (2021)

## Built on by (most cited first, 4 of 4)

- [Integrating Clinical Knowledge into Concept Bottleneck Models](https://mlsanity.com/paper/2407.06600/) (2024)
- [Deep Generative Classification of Blood Cell Morphology](https://mlsanity.com/paper/2408.08982/) (2024)
- [Training-free Test-time Improvement for Explainable Medical Image Classification](https://mlsanity.com/paper/2506.18070/) (2025)
- [Can You Trust Frozen Hematology Foundation Models under Acquisition Shift?](https://mlsanity.com/paper/2608.25148/) (2026)

## Abstract (arXiv)

Recognizing the types of white blood cells (WBCs) in microscopic images of human blood smears is a fundamental task in the fields of pathology and hematology. Although previous studies have made significant contributions to the development of methods and datasets, few papers have investigated benchmarks or baselines that others can easily refer to. For instance, we observed notable variations in the reported accuracies of the same Convolutional Neural Network (CNN) model across different studies, yet no public implementation exists to reproduce these results. In this paper, we establish a benchmark for WBC recognition. Our results indicate that CNN-based models achieve high accuracy when trained and tested under similar imaging conditions. However, their performance drops significantly when tested under different conditions. Moreover, the ResNet classifier, which has been widely employed in previous work, exhibits an unreasonably poor generalization ability under domain shifts due to batch normalization. We investigate this issue and suggest some alternative normalization techniques that can mitigate it. We make fully-reproducible code publicly availablefootnoteurlhttps://github.com/apple2373/wbc-benchmark.