MARC details
| 000 -LEADER |
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| fixed length control field |
02136nam a2200241Ia 4500 |
| 003 - CONTROL NUMBER IDENTIFIER |
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| control field |
MX-MdCICY |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
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| control field |
20251009160706.0 |
| 040 ## - CATALOGING SOURCE |
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| Transcribing agency |
CICY |
| 090 ## - LOCALLY ASSIGNED LC-TYPE CALL NUMBER (OCLC); LOCAL CALL NUMBER (RLIN) |
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| Classification number (OCLC) (R) ; Classification number, CALL (RLIN) (NR) |
B-21804 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
251009s9999 xx 000 0 und d |
| 245 10 - TITLE STATEMENT |
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| Title |
A state-of-the-art survey of U-Net in microscopic image analysis: from simple usage to structure mortification |
| 490 0# - SERIES STATEMENT |
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| Series statement |
Neural Computing and Applications, 36(7), p.3317-3346, 2024 |
| 500 ## - GENERAL NOTE |
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| General note |
Artículo |
| 520 3# - SUMMARY, ETC. |
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| Summary, etc. |
Microscopic image analysis technology helps solve the inadvertences of artificial traditional methods in disease, wastewater treatment, and environmental change monitoring analysis. Convolutional neural network (CNN) play an important role in microscopic image analysis. Image segmentation, in which U-Net is increasingly applied in microscopic image segmentation, is a crucial step in detection, tracking, monitoring, feature extraction, modelling, and analysis. This paper comprehensively reviews the development history of U-Net, analyses several research results of various segmentation methods since the emergence of U-Net, and conducts a comprehensive review of related papers. This paper summarised the improved methods of U-Net and then listed the existing significance of image segmentation techniques and their improvements introduced over the years. Finally, focusing on the different improvement strategies of U-Net in different papers, the related work of each application target is reviewed according to detailed technical categories to facilitate future research. Researchers can see the dynamics of the transmission of technological development and keep up with future trends in this interdisciplinary field. |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
MICROSCOPIC IMAGE ANALYSIS |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
U-NET |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
IMAGE SEGMENTATION |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
DEEP LEARNING |
| 650 14 - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name entry element |
CONVOLUTIONAL NEURAL NETWORK |
| 700 12 - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Wu, J., Liu;W., Li, C.;Jiang, T.;Shariful, I. M.;Yao, Y.;Grzegorzek, M. |
| 856 40 - ELECTRONIC LOCATION AND ACCESS |
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| Uniform Resource Identifier |
<a href="https://drive.google.com/file/d/1TNJkjAJM_UzNsO7arNF8pVYmkbH-3M6i/view?usp=drive_link">https://drive.google.com/file/d/1TNJkjAJM_UzNsO7arNF8pVYmkbH-3M6i/view?usp=drive_link</a> |
| Public note |
Para ver el documento ingresa a Google con tu cuenta: @cicy.edu.mx |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Clasificación local |
| Koha item type |
Documentos solicitados |
