Fine-grained imbalanced leukocyte classification with global-local attention transformer

Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts...
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Autor*in:	Ben Chen [verfasserIn] Feiwei Qin [verfasserIn] Yanli Shao [verfasserIn] Jin Cao [verfasserIn] Yong Peng [verfasserIn] Ruiquan Ge [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Leukocyte Image classification Convolutional neural network Transformer

Übergeordnetes Werk:	In: Journal of King Saud University: Computer and Information Sciences - Elsevier, 2016, 35(2023), 8, Seite 101661-
Übergeordnetes Werk:	volume:35 ; year:2023 ; number:8 ; pages:101661-

Links:	Link aufrufen Link aufrufen Link aufrufen Journal toc

DOI / URN:	10.1016/j.jksuci.2023.101661

Katalog-ID:	DOAJ095206019

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520			\|a Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer
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700	0		\|a Ruiquan Ge \|e verfasserin \|4 aut
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publishDate	2023
allfields	10.1016/j.jksuci.2023.101661 doi (DE-627)DOAJ095206019 (DE-599)DOAJee44e6ff262743b0bc3855568d779a75 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Ben Chen verfasserin aut Fine-grained imbalanced leukocyte classification with global-local attention transformer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer Leukocyte Image classification Convolutional neural network Transformer Electronic computers. Computer science Feiwei Qin verfasserin aut Yanli Shao verfasserin aut Jin Cao verfasserin aut Yong Peng verfasserin aut Ruiquan Ge verfasserin aut In Journal of King Saud University: Computer and Information Sciences Elsevier, 2016 35(2023), 8, Seite 101661- (DE-627)746705778 (DE-600)2716720-3 13191578 nnns volume:35 year:2023 number:8 pages:101661- https://doi.org/10.1016/j.jksuci.2023.101661 kostenfrei https://doaj.org/article/ee44e6ff262743b0bc3855568d779a75 kostenfrei http://www.sciencedirect.com/science/article/pii/S131915782300215X kostenfrei https://doaj.org/toc/1319-1578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 8 101661-
spelling	10.1016/j.jksuci.2023.101661 doi (DE-627)DOAJ095206019 (DE-599)DOAJee44e6ff262743b0bc3855568d779a75 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Ben Chen verfasserin aut Fine-grained imbalanced leukocyte classification with global-local attention transformer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer Leukocyte Image classification Convolutional neural network Transformer Electronic computers. Computer science Feiwei Qin verfasserin aut Yanli Shao verfasserin aut Jin Cao verfasserin aut Yong Peng verfasserin aut Ruiquan Ge verfasserin aut In Journal of King Saud University: Computer and Information Sciences Elsevier, 2016 35(2023), 8, Seite 101661- (DE-627)746705778 (DE-600)2716720-3 13191578 nnns volume:35 year:2023 number:8 pages:101661- https://doi.org/10.1016/j.jksuci.2023.101661 kostenfrei https://doaj.org/article/ee44e6ff262743b0bc3855568d779a75 kostenfrei http://www.sciencedirect.com/science/article/pii/S131915782300215X kostenfrei https://doaj.org/toc/1319-1578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 8 101661-
allfields_unstemmed	10.1016/j.jksuci.2023.101661 doi (DE-627)DOAJ095206019 (DE-599)DOAJee44e6ff262743b0bc3855568d779a75 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Ben Chen verfasserin aut Fine-grained imbalanced leukocyte classification with global-local attention transformer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer Leukocyte Image classification Convolutional neural network Transformer Electronic computers. Computer science Feiwei Qin verfasserin aut Yanli Shao verfasserin aut Jin Cao verfasserin aut Yong Peng verfasserin aut Ruiquan Ge verfasserin aut In Journal of King Saud University: Computer and Information Sciences Elsevier, 2016 35(2023), 8, Seite 101661- (DE-627)746705778 (DE-600)2716720-3 13191578 nnns volume:35 year:2023 number:8 pages:101661- https://doi.org/10.1016/j.jksuci.2023.101661 kostenfrei https://doaj.org/article/ee44e6ff262743b0bc3855568d779a75 kostenfrei http://www.sciencedirect.com/science/article/pii/S131915782300215X kostenfrei https://doaj.org/toc/1319-1578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 8 101661-
allfieldsGer	10.1016/j.jksuci.2023.101661 doi (DE-627)DOAJ095206019 (DE-599)DOAJee44e6ff262743b0bc3855568d779a75 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Ben Chen verfasserin aut Fine-grained imbalanced leukocyte classification with global-local attention transformer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer Leukocyte Image classification Convolutional neural network Transformer Electronic computers. Computer science Feiwei Qin verfasserin aut Yanli Shao verfasserin aut Jin Cao verfasserin aut Yong Peng verfasserin aut Ruiquan Ge verfasserin aut In Journal of King Saud University: Computer and Information Sciences Elsevier, 2016 35(2023), 8, Seite 101661- (DE-627)746705778 (DE-600)2716720-3 13191578 nnns volume:35 year:2023 number:8 pages:101661- https://doi.org/10.1016/j.jksuci.2023.101661 kostenfrei https://doaj.org/article/ee44e6ff262743b0bc3855568d779a75 kostenfrei http://www.sciencedirect.com/science/article/pii/S131915782300215X kostenfrei https://doaj.org/toc/1319-1578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 8 101661-
allfieldsSound	10.1016/j.jksuci.2023.101661 doi (DE-627)DOAJ095206019 (DE-599)DOAJee44e6ff262743b0bc3855568d779a75 DE-627 ger DE-627 rakwb eng QA75.5-76.95 Ben Chen verfasserin aut Fine-grained imbalanced leukocyte classification with global-local attention transformer 2023 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer Leukocyte Image classification Convolutional neural network Transformer Electronic computers. Computer science Feiwei Qin verfasserin aut Yanli Shao verfasserin aut Jin Cao verfasserin aut Yong Peng verfasserin aut Ruiquan Ge verfasserin aut In Journal of King Saud University: Computer and Information Sciences Elsevier, 2016 35(2023), 8, Seite 101661- (DE-627)746705778 (DE-600)2716720-3 13191578 nnns volume:35 year:2023 number:8 pages:101661- https://doi.org/10.1016/j.jksuci.2023.101661 kostenfrei https://doaj.org/article/ee44e6ff262743b0bc3855568d779a75 kostenfrei http://www.sciencedirect.com/science/article/pii/S131915782300215X kostenfrei https://doaj.org/toc/1319-1578 Journal toc kostenfrei GBV_USEFLAG_A SYSFLAG_A GBV_DOAJ GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_95 GBV_ILN_105 GBV_ILN_110 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2034 GBV_ILN_2038 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2056 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2088 GBV_ILN_2106 GBV_ILN_2110 GBV_ILN_2112 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2470 GBV_ILN_2507 GBV_ILN_4012 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4126 GBV_ILN_4242 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4338 GBV_ILN_4367 GBV_ILN_4393 GBV_ILN_4700 AR 35 2023 8 101661-
language	English
source	In Journal of King Saud University: Computer and Information Sciences 35(2023), 8, Seite 101661- volume:35 year:2023 number:8 pages:101661-
sourceStr	In Journal of King Saud University: Computer and Information Sciences 35(2023), 8, Seite 101661- volume:35 year:2023 number:8 pages:101661-
format_phy_str_mv	Article
institution	findex.gbv.de
topic_facet	Leukocyte Image classification Convolutional neural network Transformer Electronic computers. Computer science
isfreeaccess_bool	true
container_title	Journal of King Saud University: Computer and Information Sciences
authorswithroles_txt_mv	Ben Chen @@aut@@ Feiwei Qin @@aut@@ Yanli Shao @@aut@@ Jin Cao @@aut@@ Yong Peng @@aut@@ Ruiquan Ge @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	746705778
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language_de	englisch
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callnumber-first	Q - Science
author	Ben Chen
spellingShingle	Ben Chen misc QA75.5-76.95 misc Leukocyte misc Image classification misc Convolutional neural network misc Transformer misc Electronic computers. Computer science Fine-grained imbalanced leukocyte classification with global-local attention transformer
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topic_title	QA75.5-76.95 Fine-grained imbalanced leukocyte classification with global-local attention transformer Leukocyte Image classification Convolutional neural network Transformer
topic	misc QA75.5-76.95 misc Leukocyte misc Image classification misc Convolutional neural network misc Transformer misc Electronic computers. Computer science
topic_unstemmed	misc QA75.5-76.95 misc Leukocyte misc Image classification misc Convolutional neural network misc Transformer misc Electronic computers. Computer science
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title	Fine-grained imbalanced leukocyte classification with global-local attention transformer
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title_full	Fine-grained imbalanced leukocyte classification with global-local attention transformer
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title_sort	fine-grained imbalanced leukocyte classification with global-local attention transformer
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title_auth	Fine-grained imbalanced leukocyte classification with global-local attention transformer
abstract	Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer
abstractGer	Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer
abstract_unstemmed	Leukemia is a fatal disease that requires the counting of White Blood Cells (WBCs) in bone marrow for diagnosis. However, bone marrow blood contains many types of leukocytes, some of which have subtle differences. To address this issue, we propose the WBC-GLAformer model, which comprises three parts: Low-level Feature Extractor (LFE), Global–Local Attention based Encoder (GLAE), and Discrimination Part Select (DPS). The LFE uses a convolutional neural network (CNN) to tokenize patches from the extracted low-level features. The GLAE combines the ability of the CNN to extract local features with the ability of the transformer to extract global features, thereby enriching the features of leukocyte images. The DPS improves the accuracy of leukocyte classification by selecting the discriminative regions. Our method achieves state-of-the-art results in the bone marrow leukocyte fine-grained classification dataset. Experimental results demonstrate that the model has good generalization on different datasets and is more robust to the optimizer. And visualization results show that the model can effectively focus on the discriminative parts of different cells. Code is available at https://github.com/ywj1/WBC-GLAformer
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title_short	Fine-grained imbalanced leukocyte classification with global-local attention transformer
url	https://doi.org/10.1016/j.jksuci.2023.101661 https://doaj.org/article/ee44e6ff262743b0bc3855568d779a75 http://www.sciencedirect.com/science/article/pii/S131915782300215X https://doaj.org/toc/1319-1578
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Fine-grained imbalanced leukocyte classification with global-local attention transformer

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