SG-CLDFF: A Novel Framework for Automated White Blood Cell Classification and Segmentation

TL;DR

This paper introduces SG-CLDFF, a novel deep learning framework that combines saliency-guided preprocessing with multi-scale feature fusion for improved white blood cell segmentation and classification, emphasizing robustness and interpretability.

Contribution

The paper presents a new integrated framework that enhances WBC analysis by combining saliency-driven region highlighting with cross-layer deep feature fusion, improving accuracy and interpretability.

Findings

Improved IoU, F1, and classification accuracy on public benchmarks.

Effective mitigation of class imbalance and background activation.

Enhanced interpretability through Grad-CAM visualizations.

Abstract

Accurate segmentation and classification of white blood cells (WBCs) in microscopic images are essential for diagnosis and monitoring of many hematological disorders, yet remain challenging due to staining variability, complex backgrounds, and class imbalance. In this paper, we introduce a novel Saliency-Guided Cross-Layer Deep Feature Fusion framework (SG-CLDFF) that tightly integrates saliency-driven preprocessing with multi-scale deep feature aggregation to improve both robustness and interpretability for WBC analysis. SG-CLDFF first computes saliency priors to highlight candidate WBC regions and guide subsequent feature extraction. A lightweight hybrid backbone (EfficientSwin-style) produces multi-resolution representations, which are fused by a ResNeXt-CC-inspired cross-layer fusion module to preserve complementary information from shallow and deep layers. The network is trained in a multi-task setup with concurrent segmentation and cell-type classification heads, using class-aware weighted losses and saliency-alignment regularization to mitigate imbalance and suppress background activation. Interpretability is enforced through Grad-CAM visualizations and saliency consistency checks, allowing model decisions to be inspected at the regional level. We validate the framework on standard public benchmarks (BCCD, LISC, ALL-IDB), reporting consistent gains in IoU, F1, and classification accuracy compared to strong CNN and transformer baselines. An ablation study also demonstrates the individual contributions of saliency preprocessing and cross-layer fusion. SG-CLDFF offers a practical and explainable path toward more reliable automated WBC analysis in clinical workflows.