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Attention-aware Residual Network based Manifold Learning for White Blood Cells Classification.

Abstract：

:The classification of six types of white blood cells (WBCs) is considered essential for leukemia diagnosis, while the classification is labor-intensive and strict with the clinical experience. To relieve the complicated process with an efficient and automatic method, we propose the Attention-aware Residual Network based Manifold Learning model (ARML) to classify WBCs. The proposed ARML model leverages the adaptive attention-aware residual learning to exploit the category-relevant image-level features and strengthen the first-order feature representation ability. To learn more discriminatory information than the first-order ones, the second-order features are characterized. Afterwards, ARML encodes both the first- and second-order features with Gaussian embedding into the Riemannian manifold to learn the underlying non-linear structure of the features for classification. ARML can be trained in an end-to-end fashion, and the learnable parameters are iteratively optimized. 10800 WBCs images (1800 images for each type) is collected, 9000 images and five-fold cross-validation are used for training and validation of the model, while additional 1800 images for testing. The results show that ARML achieving average classification accuracy of 0.953 outperforms other state-of-the-art methods with fewer trainable parameters. In the ablation study, ARML achieves improved accuracy against its three variants: without manifold learning (AR), without attention-aware learning (RML), and AR without attention-aware learning. The t-SNE results illustrate that ARML has learned more distinguishable features than the comparison methods, which benefits the WBCs classification. ARML provides a clinically feasible WBCs classification solution for leukemia diagnose with an efficient manner.

journal_name

IEEE J Biomed Health Inform

journal_title

IEEE journal of biomedical and health informatics

authors

Huang P,Wang J,Zhang J,Shen Y,Liu C,Song W,Wu S,Zuo Y,Lu Z,Li D

doi

10.1109/JBHI.2020.3012711

subject

Has Abstract

pub_date

2020-07-29 00:00:00

eissn

2168-2194

issn

2168-2208

journal_volume

pub_type

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Attention-aware Residual Network based Manifold Learning for White Blood Cells Classification.