國立高雄大學圖資館 |

Language: English

Back

ISBI 2019 C-NMC challengeclassificat...

Gupta, Anubha.

ISBI 2019 C-NMC challengeclassification in cancer cell imaging : select proceedings /

Record Type:	Electronic resources : Monograph/item
Title/Author:	ISBI 2019 C-NMC challengeedited by Anubha Gupta, Ritu Gupta.
Reminder of title:	classification in cancer cell imaging : select proceedings /
other author:	Gupta, Anubha.
Published:	Singapore :Springer Singapore :2019.
Description:	x, 147 p. :ill., digital ;24 cm.
Contained By:	Springer eBooks
Subject:	Cancer cellsHandbooks, manuals, etc.
Online resource:	https://doi.org/10.1007/978-981-15-0798-4
ISBN:	9789811507984$q(electronic bk.)

ISBI 2019 C-NMC challengeclassification in cancer cell imaging : select proceedings /
ISBI 2019 C-NMC challengeclassification in cancer cell imaging : select proceedings /[electronic resource] :edited by Anubha Gupta, Ritu Gupta. - Singapore :Springer Singapore :2019. - x, 147 p. :ill., digital ;24 cm. - Lecture notes in bioengineering,2195-271X. - Lecture notes in bioengineering..

This book comprises select peer-reviewed proceedings of the medical challenge - C-NMC challenge: Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. The challenge was run as part of the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) 2019 held at Venice, Italy in April 2019. Cell classification via image processing has recently gained interest from the point of view of building computer-assisted diagnostic tools for blood disorders such as leukaemia. In order to arrive at a conclusive decision on disease diagnosis and degree of progression, it is very important to identify malignant cells with high accuracy. Computer-assisted tools can be very helpful in automating the process of cell segmentation and identification because morphologically both cell types appear similar. This particular challenge was run on a curated data set of more than 14000 cell images of very high quality. More than 200 international teams participated in the challenge. This book covers various solutions using machine learning and deep learning approaches. The book will prove useful for academics, researchers, and professionals interested in building low-cost automated diagnostic tools for cancer diagnosis and treatment.

ISBN: 9789811507984$q(electronic bk.)

Standard No.: 10.1007/978-981-15-0798-4doiSubjects--Topical Terms:

502015
Cancer cells
--Handbooks, manuals, etc.

LC Class. No.: RC269 / .I835 2019

Dewey Class. No.: 616.99407

ISBI 2019 C-NMC challengeclassification in cancer cell imaging : select proceedings /
LDR:02340nmm a2200325 a 4500 001 569218
003 DE-He213
005 20200205134114.0
006 m d
007 cr nn 008maaau
008 200723s2019 si s 0 eng d
020 $a 9789811507984$q(electronic bk.)
020 $a 9789811507977$q(paper)
024 7 $a 10.1007/978-981-15-0798-4 $2 doi
035 $a 978-981-15-0798-4
040 $a GP $c GP
041 0 $a eng
050 4 $a RC269 $b .I835 2019
072 7 $a MJCL $2 bicssc
072 7 $a MED062000 $2 bisacsh
072 7 $a MJCL $2 thema
082 0 4 $a 616.99407 $2 23
090 $a RC269 $b .I76 2019
245 0 0 $a ISBI 2019 C-NMC challenge $h [electronic resource] : $b classification in cancer cell imaging : select proceedings / $c edited by Anubha Gupta, Ritu Gupta.
260 $a Singapore : $b Springer Singapore : $b Imprint: Springer, $c 2019.
300 $a x, 147 p. : $b ill., digital ; $c 24 cm.
490 1 $a Lecture notes in bioengineering, $x 2195-271X
520 $a This book comprises select peer-reviewed proceedings of the medical challenge - C-NMC challenge: Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. The challenge was run as part of the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) 2019 held at Venice, Italy in April 2019. Cell classification via image processing has recently gained interest from the point of view of building computer-assisted diagnostic tools for blood disorders such as leukaemia. In order to arrive at a conclusive decision on disease diagnosis and degree of progression, it is very important to identify malignant cells with high accuracy. Computer-assisted tools can be very helpful in automating the process of cell segmentation and identification because morphologically both cell types appear similar. This particular challenge was run on a curated data set of more than 14000 cell images of very high quality. More than 200 international teams participated in the challenge. This book covers various solutions using machine learning and deep learning approaches. The book will prove useful for academics, researchers, and professionals interested in building low-cost automated diagnostic tools for cancer diagnosis and treatment.
650 0 $a Cancer cells $v Handbooks, manuals, etc. $3 502015
650 1 4 $a Cancer Research. $3 273660
650 2 4 $a Biomedical Engineering and Bioengineering. $3 826326
650 2 4 $a Signal, Image and Speech Processing. $3 273768
650 2 4 $a Computer Imaging, Vision, Pattern Recognition and Graphics. $3 274492
700 1 $a Gupta, Anubha. $3 855127
700 1 $a Gupta, Ritu. $3 855128
710 2 $a SpringerLink (Online service) $3 273601
773 0 $t Springer eBooks
830 0 $a Lecture notes in bioengineering. $3 676120
856 4 0 $u https://doi.org/10.1007/978-981-15-0798-4
950 $a Biomedical and Life Sciences (Springer-11642)