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ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging = Select Proceedings /

紀錄類型:	書目-語言資料,印刷品 : Monograph/item
正題名/作者:	ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging/ edited by Anubha Gupta, Ritu Gupta.
其他題名:	Select Proceedings /
其他作者:	Gupta, Anubha.
面頁冊數:	X, 147 p. 64 illus., 61 illus. in color.online resource. :
Contained By:	Springer Nature eBook
標題:	Cancer research. -
電子資源:	https://doi.org/10.1007/978-981-15-0798-4
ISBN:	9789811507984

ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging = Select Proceedings /
ISBI 2019 C-NMC Challenge: Classification in Cancer Cell ImagingSelect Proceedings /[electronic resource] :edited by Anubha Gupta, Ritu Gupta. - 1st ed. 2019. - X, 147 p. 64 illus., 61 illus. in color.online resource. - Lecture Notes in Bioengineering,2195-271X. - Lecture Notes in Bioengineering,.

Chapter 1: Classiﬁcation of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images -- Chapter 2: Classiﬁcation of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques -- Chapter 3: .

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

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

1253664
Cancer research.

LC Class. No.: RC261-271

Dewey Class. No.: 614.5999

ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging = Select Proceedings /
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