# Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification

arXiv: https://arxiv.org/abs/2207.00501 · HTML: https://mlsanity.com/paper/2207.00501/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 19 references that are in the map.
- First posted on arXiv 1 Jul 2022.

## Builds on (most cited first, 9 of 9)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Mask R-CNN](https://mlsanity.com/paper/1703.06870/) (2017)
- [Feature Pyramid Networks for Object Detection](https://mlsanity.com/paper/1612.03144/) (2016)
- [UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction](https://mlsanity.com/paper/1802.03426/) (2018)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Group Normalization](https://mlsanity.com/paper/1803.08494/) (2018)
- [Adversarial Discriminative Domain Adaptation](https://mlsanity.com/paper/1702.05464/) (2017)
- [Domain Generalization via Model-Agnostic Learning of Semantic Features](https://mlsanity.com/paper/1910.13580/) (2019)
- [Unsupervised Bidirectional Cross-Modality Adaptation via Deeply Synergistic Image and Feature Alignment for Medical Image Segmentation](https://mlsanity.com/paper/2002.02255/) (2020)

## Built on by (most cited first, 10 of 10)

- [A Continual Learning Approach for Cross-Domain White Blood Cell Classification](https://mlsanity.com/paper/2308.12679/) (2023)
- [Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology](https://mlsanity.com/paper/2303.07771/) (2023)
- [Neural Cellular Automata for Lightweight, Robust and Explainable Classification of White Blood Cell Images](https://mlsanity.com/paper/2404.05584/) (2024)
- [Towards Cross-Domain Single Blood Cell Image Classification via Large-Scale LoRA-based Segment Anything Model](https://mlsanity.com/paper/2408.06716/) (2024)
- [Neural Cellular Automata for Weakly Supervised Segmentation of White Blood Cells](https://mlsanity.com/paper/2508.12322/) (2025)
- [Attention Pooling Enhances NCA-based Classification of Microscopy Images](https://mlsanity.com/paper/2508.12324/) (2025)
- [A Study of Age and Sex Bias in Multiple Instance Learning based Classification of Acute Myeloid Leukemia Subtypes](https://mlsanity.com/paper/2308.12675/) (2023)
- [Multimodal Analysis of White Blood Cell Differentiation in Acute Myeloid Leukemia Patients using a β-Variational Autoencoder](https://mlsanity.com/paper/2408.06720/) (2024)
- [Domain-invariant Representation Learning via Segment Anything Model for Blood Cell Classification](https://mlsanity.com/paper/2408.07467/) (2024)
- [Continual Multiple Instance Learning for Hematologic Disease Diagnosis](https://mlsanity.com/paper/2508.04368/) (2025)

## Abstract (arXiv)

Diagnosing hematological malignancies requires identification and classification of white blood cells in peripheral blood smears. Domain shifts caused by different lab procedures, staining, illumination, and microscope settings hamper the re-usability of recently developed machine learning methods on data collected from different sites. Here, we propose a cross-domain adapted autoencoder to extract features in an unsupervised manner on three different datasets of single white blood cells scanned from peripheral blood smears. The autoencoder is based on an R-CNN architecture allowing it to focus on the relevant white blood cell and eliminate artifacts in the image. To evaluate the quality of the extracted features we use a simple random forest to classify single cells. We show that thanks to the rich features extracted by the autoencoder trained on only one of the datasets, the random forest classifier performs satisfactorily on the unseen datasets, and outperforms published oracle networks in the cross-domain task. Our results suggest the possibility of employing this unsupervised approach in more complicated diagnosis and prognosis tasks without the need to add expensive expert labels to unseen data.