LEADER 04319nam 22007095 450 001 9910373912203321 005 20220323203755.0 010 $a981-15-0798-8 024 7 $a10.1007/978-981-15-0798-4 035 $a(CKB)4100000009939698 035 $a(MiAaPQ)EBC5987246 035 $a(DE-He213)978-981-15-0798-4 035 $a(PPN)260301701 035 $a(EXLCZ)994100000009939698 100 $a20191128d2019 u| 0 101 0 $aeng 135 $aurcnu|||||||| 181 $ctxt$2rdacontent 182 $cc$2rdamedia 183 $acr$2rdacarrier 200 10$aISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging $eSelect Proceedings /$fedited by Anubha Gupta, Ritu Gupta 205 $a1st ed. 2019. 210 1$aSingapore :$cSpringer Singapore :$cImprint: Springer,$d2019. 215 $a1 online resource (150 pages) 225 1 $aLecture Notes in Bioengineering,$x2195-271X 311 $a981-15-0797-X 327 $aChapter 1: Classi?cation of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images -- Chapter 2: Classi?cation of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques -- Chapter 3: . 330 $aThis book comprises select peer-reviewed proceedings of the medical challenge - C-NMC challenge: Classification of normal versus malignant cells in B-ALL white blood cancer microscopic images. The challenge was run as part of the IEEE International Symposium on Biomedical Imaging (IEEE ISBI) 2019 held at Venice, Italy in April 2019. Cell classification via image processing has recently gained interest from the point of view of building computer-assisted diagnostic tools for blood disorders such as leukaemia. In order to arrive at a conclusive decision on disease diagnosis and degree of progression, it is very important to identify malignant cells with high accuracy. Computer-assisted tools can be very helpful in automating the process of cell segmentation and identification because morphologically both cell types appear similar. This particular challenge was run on a curated data set of more than 14000 cell images of very high quality. More than 200 international teams participated in the challenge. This book covers various solutions using machine learning and deep learning approaches. The book will prove useful for academics, researchers, and professionals interested in building low-cost automated diagnostic tools for cancer diagnosis and treatment. 410 0$aLecture Notes in Bioengineering,$x2195-271X 606 $aCancer$xResearch 606 $aBiomedical engineering 606 $aSignal processing 606 $aImage processing 606 $aSpeech processing systems 606 $aOptical data processing 606 $aCancer Research$3https://scigraph.springernature.com/ontologies/product-market-codes/B11001 606 $aBiomedical Engineering and Bioengineering$3https://scigraph.springernature.com/ontologies/product-market-codes/T2700X 606 $aSignal, Image and Speech Processing$3https://scigraph.springernature.com/ontologies/product-market-codes/T24051 606 $aComputer Imaging, Vision, Pattern Recognition and Graphics$3https://scigraph.springernature.com/ontologies/product-market-codes/I22005 606 $aCèl·lules canceroses$2thub 608 $aLlibres electrònics$2thub 615 0$aCancer$xResearch. 615 0$aBiomedical engineering. 615 0$aSignal processing. 615 0$aImage processing. 615 0$aSpeech processing systems. 615 0$aOptical data processing. 615 14$aCancer Research. 615 24$aBiomedical Engineering and Bioengineering. 615 24$aSignal, Image and Speech Processing. 615 24$aComputer Imaging, Vision, Pattern Recognition and Graphics. 615 7$aCèl·lules canceroses 676 $a616.99407 702 $aGupta$b Anubha$4edt$4http://id.loc.gov/vocabulary/relators/edt 702 $aGupta$b Ritu$4edt$4http://id.loc.gov/vocabulary/relators/edt 801 0$bMiAaPQ 801 1$bMiAaPQ 801 2$bMiAaPQ 906 $aBOOK 912 $a9910373912203321 996 $aISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging$92184173 997 $aUNINA