Corruption-Robust Enhancement of Deep Neural Networks for Classification of Peripheral Blood Smear Images

Zhang，Songtao1; Ni，Qingwen1; Li，Bing2; Jiang，Shan1; Cai，Wenyu3; Chen，Hang1; Luo，Lin1,4

10.1007/978-3-030-59722-1_36

2020

0302-9743

1611-3349

LECT NOTES ARTIF INT

12265 LNCS

372-381

Deep learning emerges as a promising technology for automated peripheral blood smear analysis and hematologic diagnosis. A big challenge to deep learning model is the accuracy drop when facing image corruptions caused by different smear preparation and digitalization operations. In order to serve the real applications of peripheral blood analysis, a practical deep learning classifier needs to be robust against a wide range of corruptions. In this paper, we first investigate the comprehensive corruption types for peripheral blood smear image and establish the benchmark dataset Smear-C simulating the real physical cause factors. Then we propose a novel method SmearRobust which can easily fit into the existing neural networks and improve the robustness. Experimental results show that SmearRobust can significantly enhance the robustness upon Smear-C dataset. Furthermore, the proposed corruption simulation algorithms and the robust learning method can be potentially applied to bone marrow smear and general pathology areas.

Corruption Deep learning Peripheral blood Robustness

通讯

英语

EI

20204309372877

Blood ; Learning systems ; Crime ; Image classification ; Diagnosis ; Image enhancement

Biological Materials and Tissue Engineering:461.2 ; Ergonomics and Human Factors Engineering:461.4 ; Medicine and Pharmacology:461.6 ; Data Processing and Image Processing:723.2 ; Social Sciences:971

2-s2.0-85092730693

Scopus

1.College of Engineering,Peking University,Beijing,China
2.MDS and MPN Centre,Institute of Haematology and Blood Diseases Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Tianjin,China
3.Department of Pathology,Institute of Haematology and Blood Diseases Hospital,Chinese Academy of Medical Sciences and Peking Union Medical College,Tianjin,China
4.IE-School,Southern University of Science and Technology,Shenzhen,China

Zhang，Songtao,Ni，Qingwen,Li，Bing,et al. Corruption-Robust Enhancement of Deep Neural Networks for Classification of Peripheral Blood Smear Images[C],2020:372-381.