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Classification of Leukocytes

Classification of Leukocytes

Leukocytes are classified into two groups based on the presence or absence of cytoplasmic granules. Granular leukocytes, which contain granules, belong to the myeloid lineage and are divided into three subtypes: neutrophils, eosinophils, and basophils. These cells are roughly spherical and characterized by the granules in their cytoplasm.
Neutrophils are the most abundant type of granular leukocytes, comprising 50-70% of all leukocytes. They feature small, evenly distributed granules and a...

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Test-Time Augmentation for Cross-Domain Leukocyte Classification via OOD Filtering and Self-Ensembling.

Lorenzo Putzu¹, Andrea Loddo², Cecilia Di Ruberto²

¹Department of Electrical and Electronic Engineering, University of Cagliari, 09123 Cagliari, Italy.

Journal of Imaging

|September 26, 2025

Summary

This study introduces a method to improve machine learning model robustness against domain shift by filtering unrealistic augmented data. The approach enhances prediction accuracy by weighting reliable test-time augmentations and using a self-ensemble strategy.

Keywords:

domain generalisation domain shift late fusion leukocyte classification self-ensemble test-time augmentation weighted voting

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Area of Science:

Machine Learning
Computer Vision
Medical Imaging

Background:

Domain shift, caused by variations in data acquisition, presents a significant challenge in machine learning, especially in medical applications.
Test-time augmentation (TTA) is a technique used to enhance model robustness by aggregating predictions from multiple augmented data samples.
Standard TTA can inadvertently introduce out-of-distribution (OOD) samples, negatively impacting prediction accuracy.

Purpose of the Study:

To develop a novel method for filtering OOD samples generated during TTA.
To improve the robustness and accuracy of machine learning models in the presence of domain shift.
To introduce a lightweight self-ensemble strategy for enhanced prediction fusion.

Main Methods:

A filtering procedure was implemented to identify and remove OOD samples from TTA images based on their distance from the training data distribution.
Retained TTA images were weighted inversely proportional to their distance from the training data distribution.
A Self-Ensemble with Confidence strategy was employed, fusing predictions from original and filtered TTA samples using weighted soft voting.

Main Results:

The proposed method demonstrated consistent improvements over standard TTA and baseline inference on cross-domain leukocyte classification tasks.
Effectiveness was particularly notable under conditions of strong domain shift.
Ablation studies and statistical analyses confirmed the significant contribution of each component of the proposed method.

Conclusions:

The developed filtering and weighting strategy effectively mitigates the negative impact of OOD samples in TTA.
The Self-Ensemble with Confidence approach provides a model-agnostic and computationally efficient way to improve prediction accuracy.
This method offers broad applicability across various machine learning domains facing domain shift challenges.