Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear

Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual informatio...
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Gespeichert in:

Autor*in:	Yu, Bo [verfasserIn] Yin, Peng [verfasserIn] Chen, Hechang [verfasserIn] Wang, Yifei [verfasserIn] Zhao, Yu [verfasserIn] Cong, Xianling [verfasserIn] Dijkstra, Jouke [verfasserIn] Cong, Lele [verfasserIn]

Format:	E-Artikel
Sprache:	Englisch

Erschienen:	2023

Schlagwörter:	Pyramid feature Cytological smear Vision transformer Multiple instance learning Thyroid cancer

Übergeordnetes Werk:	Enthalten in: Knowledge-based systems - Amsterdam [u.a.] : Elsevier Science, 1987, 275
Übergeordnetes Werk:	volume:275

DOI / URN:	10.1016/j.knosys.2023.110721

Katalog-ID:	ELV060272767

Internformat


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245	1	0	\|a Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear
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520			\|a Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears.
650		4	\|a Pyramid feature
650		4	\|a Cytological smear
650		4	\|a Vision transformer
650		4	\|a Multiple instance learning
650		4	\|a Thyroid cancer
700	1		\|a Yin, Peng \|e verfasserin \|4 aut
700	1		\|a Chen, Hechang \|e verfasserin \|0 (orcid)0000-0001-7835-9556 \|4 aut
700	1		\|a Wang, Yifei \|e verfasserin \|4 aut
700	1		\|a Zhao, Yu \|e verfasserin \|4 aut
700	1		\|a Cong, Xianling \|e verfasserin \|4 aut
700	1		\|a Dijkstra, Jouke \|e verfasserin \|0 (orcid)0000-0002-8666-3731 \|4 aut
700	1		\|a Cong, Lele \|e verfasserin \|4 aut
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951			\|a AR
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Indexfelder

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publishDate	2023
allfields	10.1016/j.knosys.2023.110721 doi (DE-627)ELV060272767 (ELSEVIER)S0950-7051(23)00471-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yu, Bo verfasserin aut Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears. Pyramid feature Cytological smear Vision transformer Multiple instance learning Thyroid cancer Yin, Peng verfasserin aut Chen, Hechang verfasserin (orcid)0000-0001-7835-9556 aut Wang, Yifei verfasserin aut Zhao, Yu verfasserin aut Cong, Xianling verfasserin aut Dijkstra, Jouke verfasserin (orcid)0000-0002-8666-3731 aut Cong, Lele verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 275 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:275 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 275
spelling	10.1016/j.knosys.2023.110721 doi (DE-627)ELV060272767 (ELSEVIER)S0950-7051(23)00471-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yu, Bo verfasserin aut Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears. Pyramid feature Cytological smear Vision transformer Multiple instance learning Thyroid cancer Yin, Peng verfasserin aut Chen, Hechang verfasserin (orcid)0000-0001-7835-9556 aut Wang, Yifei verfasserin aut Zhao, Yu verfasserin aut Cong, Xianling verfasserin aut Dijkstra, Jouke verfasserin (orcid)0000-0002-8666-3731 aut Cong, Lele verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 275 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:275 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 275
allfields_unstemmed	10.1016/j.knosys.2023.110721 doi (DE-627)ELV060272767 (ELSEVIER)S0950-7051(23)00471-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yu, Bo verfasserin aut Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears. Pyramid feature Cytological smear Vision transformer Multiple instance learning Thyroid cancer Yin, Peng verfasserin aut Chen, Hechang verfasserin (orcid)0000-0001-7835-9556 aut Wang, Yifei verfasserin aut Zhao, Yu verfasserin aut Cong, Xianling verfasserin aut Dijkstra, Jouke verfasserin (orcid)0000-0002-8666-3731 aut Cong, Lele verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 275 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:275 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 275
allfieldsGer	10.1016/j.knosys.2023.110721 doi (DE-627)ELV060272767 (ELSEVIER)S0950-7051(23)00471-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yu, Bo verfasserin aut Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears. Pyramid feature Cytological smear Vision transformer Multiple instance learning Thyroid cancer Yin, Peng verfasserin aut Chen, Hechang verfasserin (orcid)0000-0001-7835-9556 aut Wang, Yifei verfasserin aut Zhao, Yu verfasserin aut Cong, Xianling verfasserin aut Dijkstra, Jouke verfasserin (orcid)0000-0002-8666-3731 aut Cong, Lele verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 275 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:275 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 275
allfieldsSound	10.1016/j.knosys.2023.110721 doi (DE-627)ELV060272767 (ELSEVIER)S0950-7051(23)00471-9 DE-627 ger DE-627 rda eng 004 VZ 54.72 bkl Yu, Bo verfasserin aut Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear 2023 nicht spezifiziert zzz rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears. Pyramid feature Cytological smear Vision transformer Multiple instance learning Thyroid cancer Yin, Peng verfasserin aut Chen, Hechang verfasserin (orcid)0000-0001-7835-9556 aut Wang, Yifei verfasserin aut Zhao, Yu verfasserin aut Cong, Xianling verfasserin aut Dijkstra, Jouke verfasserin (orcid)0000-0002-8666-3731 aut Cong, Lele verfasserin aut Enthalten in Knowledge-based systems Amsterdam [u.a.] : Elsevier Science, 1987 275 Online-Ressource (DE-627)320580024 (DE-600)2017495-0 (DE-576)253018722 0950-7051 nnns volume:275 GBV_USEFLAG_U GBV_ELV SYSFLAG_U GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_150 GBV_ILN_151 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_370 GBV_ILN_602 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 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_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_2111 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_4035 GBV_ILN_4037 GBV_ILN_4112 GBV_ILN_4125 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_4338 GBV_ILN_4393 GBV_ILN_4700 54.72 Künstliche Intelligenz VZ AR 275
language	English
source	Enthalten in Knowledge-based systems 275 volume:275
sourceStr	Enthalten in Knowledge-based systems 275 volume:275
format_phy_str_mv	Article
bklname	Künstliche Intelligenz
institution	findex.gbv.de
topic_facet	Pyramid feature Cytological smear Vision transformer Multiple instance learning Thyroid cancer
dewey-raw	004
isfreeaccess_bool	false
container_title	Knowledge-based systems
authorswithroles_txt_mv	Yu, Bo @@aut@@ Yin, Peng @@aut@@ Chen, Hechang @@aut@@ Wang, Yifei @@aut@@ Zhao, Yu @@aut@@ Cong, Xianling @@aut@@ Dijkstra, Jouke @@aut@@ Cong, Lele @@aut@@
publishDateDaySort_date	2023-01-01T00:00:00Z
hierarchy_top_id	320580024
dewey-sort	14
id	ELV060272767
language_de	englisch
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author	Yu, Bo
spellingShingle	Yu, Bo ddc 004 bkl 54.72 misc Pyramid feature misc Cytological smear misc Vision transformer misc Multiple instance learning misc Thyroid cancer Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear
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topic_title	004 VZ 54.72 bkl Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear Pyramid feature Cytological smear Vision transformer Multiple instance learning Thyroid cancer
topic	ddc 004 bkl 54.72 misc Pyramid feature misc Cytological smear misc Vision transformer misc Multiple instance learning misc Thyroid cancer
topic_unstemmed	ddc 004 bkl 54.72 misc Pyramid feature misc Cytological smear misc Vision transformer misc Multiple instance learning misc Thyroid cancer
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title	Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear
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title_full	Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear
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title_sort	pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear
title_auth	Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear
abstract	Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears.
abstractGer	Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears.
abstract_unstemmed	Multi-instance learning, a commonly used technique in artificial intelligence for analyzing slides, can be applied to diagnose thyroid cancer based on cytological smears. Since smears do not have multidimensional histological features similar to histopathology, mining potential contextual information and diversity of features is crucial for better classification performance. In this paper, we propose a pyramid multi-loss vision transformer model called PyMLViT, a novel algorithm with two core modules to address these issues. Specifically, we design a pyramid token extraction module to acquire potential contextual information on smears. The pyramid token structure extracts multi-scale local features, and the vision transformer structure further obtains global information through the self-attention mechanism. Furthermore, we construct multi-loss fusion module based on the conventional multi-instance learning framework. With carefully designed bag and patch weight allocation strategies, we incorporate slide-level annotations as pseudo-labels for patches to participate in training, thus enhancing the diversity of supervised information. Extensive experimental results on the real-world dataset show that PyMLViT has a high performance and a competitive number of parameters compared to popular methods for diagnosing thyroid cancer in cytological smears.
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title_short	Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear
remote_bool	true
author2	Yin, Peng Chen, Hechang Wang, Yifei Zhao, Yu Cong, Xianling Dijkstra, Jouke Cong, Lele
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doi_str	10.1016/j.knosys.2023.110721
up_date	2024-07-06T23:42:40.410Z
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Pyramid multi-loss vision transformer for thyroid cancer classification using cytological smear

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