# Imbalanced Domain Generalization for Robust Single Cell Classification in Hematological Cytomorphology

arXiv: https://arxiv.org/abs/2303.07771 · HTML: https://mlsanity.com/paper/2303.07771/

## Key facts

- Cited by 7 papers in the map (data through 14 Oct 2026).
- Builds on 14 of its 24 references that are in the map.
- First posted on arXiv 14 Mar 2023.

## Builds on (most cited first, 14 of 14)

- [Deep Residual Learning for Image Recognition](https://mlsanity.com/paper/1512.03385/) (2015)
- [Language Models are Few-Shot Learners](https://mlsanity.com/paper/2005.14165/) (2020)
- [Domain-Adversarial Training of Neural Networks](https://mlsanity.com/paper/1505.07818/) (2015)
- [Aggregated Residual Transformations for Deep Neural Networks](https://mlsanity.com/paper/1611.05431/) (2016)
- [Deep CORAL: Correlation Alignment for Deep Domain Adaptation](https://mlsanity.com/paper/1607.01719/) (2016)
- [Learning Imbalanced Datasets with Label-Distribution-Aware Margin Loss](https://mlsanity.com/paper/1906.07413/) (2019)
- [Generalizing to Unseen Domains: A Survey on Domain Generalization](https://mlsanity.com/paper/2103.03097/) (2021)
- [Domain Generalization: A Survey](https://mlsanity.com/paper/2103.02503/) (2021)
- [A systematic study of the class imbalance problem in convolutional neural networks](https://mlsanity.com/paper/1710.05381/) (2017)
- [Rethinking the Value of Labels for Improving Class-Imbalanced Learning](https://mlsanity.com/paper/2006.07529/) (2020)
- [Machine learning methods for histopathological image analysis](https://mlsanity.com/paper/1709.00786/) (2017)
- [Resolving challenges in deep learning-based analyses of histopathological images using explanation methods](https://mlsanity.com/paper/1908.06943/) (2019)
- [On Multi-Domain Long-Tailed Recognition, Imbalanced Domain Generalization and Beyond](https://mlsanity.com/paper/2203.09513/) (2022)
- [Unsupervised Cross-Domain Feature Extraction for Single Blood Cell Image Classification](https://mlsanity.com/paper/2207.00501/) (2022)

## Built on by (most cited first, 7 of 7)

- [CoTCoNet: An Optimized Coupled Transformer-Convolutional Network with an Adaptive Graph Reconstruction for Leukemia Detection](https://mlsanity.com/paper/2410.08797/) (2024)
- [Brain Anatomy Prior Modeling to Forecast Clinical Progression of Cognitive Impairment with Structural MRI](https://mlsanity.com/paper/2306.11837/) (2023)
- [Federated Learning for Data and Model Heterogeneity in Medical Imaging](https://mlsanity.com/paper/2308.00155/) (2023)
- [CytoDiff: AI-Driven Cytomorphology Image Synthesis for Medical Diagnostics](https://mlsanity.com/paper/2507.05063/) (2025)
- [Stain-aware Domain Alignment for Imbalance Blood Cell Classification](https://mlsanity.com/paper/2412.02976/) (2024)
- [Negatives-Dominant Contrastive Learning for Generalization in Imbalanced Domains](https://mlsanity.com/paper/2601.21999/) (2026)
- [Retrieval-Augmented Vision Foundation Models for Robust Leukemia Cell Classification across Multiple Microscopy Datasets](https://mlsanity.com/paper/2608.10657/) (2026)

## Abstract (arXiv)

Accurate morphological classification of white blood cells (WBCs) is an important step in the diagnosis of leukemia, a disease in which nonfunctional blast cells accumulate in the bone marrow. Recently, deep convolutional neural networks (CNNs) have been successfully used to classify leukocytes by training them on single-cell images from a specific domain. Most CNN models assume that the distributions of the training and test data are similar, i.e., the data are independently and identically distributed. Therefore, they are not robust to different staining procedures, magnifications, resolutions, scanners, or imaging protocols, as well as variations in clinical centers or patient cohorts. In addition, domain-specific data imbalances affect the generalization performance of classifiers. Here, we train a robust CNN for WBC classification by addressing cross-domain data imbalance and domain shifts. To this end, we use two loss functions and demonstrate their effectiveness in out-of-distribution (OOD) generalization. Our approach achieves the best F1 macro score compared to other existing methods and is able to consider rare cell types. This is the first demonstration of imbalanced domain generalization in hematological cytomorphology and paves the way for robust single cell classification methods for the application in laboratories and clinics.