Zobrazeno 1 - 10 of 26 pro vyhledávání: '"Ajith Kumar, V"'
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Akademický článek
Circle-U-Net: An Efficient Architecture for Semantic Segmentation
Autor: Feng Sun, Ajith Kumar V, Guanci Yang, Ansi Zhang, Yiyun Zhang
Publikováno v: Algorithms, Vol 14, Iss 6, p 159 (2021)
State-of-the-art semantic segmentation methods rely too much on complicated deep networks and thus cannot train efficiently. This paper introduces a novel Circle-U-Net architecture that exceeds the original U-Net on several standards. The proposed mo
Externí odkaz: https://doaj.org/article/fc1e10aed8d54b05bf2a8f6cde1c9e89
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5
CLEAR CELL VARIANT OF SQUAMOUS CELL CARCINOMA PENIS – A CASE REPORT
Autor: Treesa Thomas, Ajith Kumar V R, Prema K R
Publikováno v: PARIPEX INDIAN JOURNAL OF RESEARCH. :19-20
Carcinoma penis is a rare malignancy in males. The World Health Organization Classification of Tumors of the Urinary System and Male Genital Organs classifies squamous cell carcinoma of the penis into human papillomavirus (HPV) and non HPV related. A
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::ab3f4865beb4dbc1601533bb3bbdeb0c
https://doi.org/10.36106/paripex/1200701
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6
Outcome Of Newborn Hearing Screening Program in A Tertiary Care Centre, South India
Autor: Sija, S., Gireeshan, V. K., Ajith Kumar, V. T., Professor and Head of the Department, Rajesh, T. V., Additional Professor
Publikováno v: Journal of Early Hearing Detection and Intervention
Objective To determine the coverage and outcome of Universal Newborn Hearing Screening (UNHS) Program in Government Medical College Manjeri, South India. To study the prevalence of hearing loss (HL) among those children screened, to describe the char
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=od______1459::3367060cf362629ba7af2173dd216fb5
https://digitalcommons.usu.edu/jehdi/vol7/iss1/9
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8
Circle-U-Net: An Efficient Architecture for Semantic Segmentation
Autor: Ansi Zhang, Feng Sun, Guanci Yang, Yiyun Zhang, Ajith Kumar V
Publikováno v: Algorithms
Volume 14
Issue 6
Algorithms, Vol 14, Iss 159, p 159 (2021)
State-of-the-art semantic segmentation methods rely too much on complicated deep networks and thus cannot train efficiently. This paper introduces a novel Circle-U-Net architecture that exceeds the original U-Net on several standards. The proposed mo
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::90fb1325d41f3a0b861e4db1051fbb8b
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Plný text Recenzováno Digitální knihovna AV ČR
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