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ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging

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Cover of 'ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging'

Table of Contents

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    Book Overview
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    Chapter 1 Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images
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    Chapter 2 Classification of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques
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    Chapter 3 Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutional and Recurrent Neural Networks
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    Chapter 4 Deep Learning for Classifying of White Blood Cancer
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    Chapter 5 Ensemble Convolutional Neural Networks for Cell Classification in Microscopic Images
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    Chapter 6 Acute Lymphoblastic Leukemia Classification from Microscopic Images Using Convolutional Neural Networks
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    Chapter 7 Toward Automated Classification of B-Acute Lymphoblastic Leukemia
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    Chapter 8 Neighborhood-Correction Algorithm for Classification of Normal and Malignant Cells
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    Chapter 9 DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification
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    Chapter 10 Multi-streams and Multi-features for Cell Classification
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    Chapter 11 Classification of Normal Versus Malignant Cells in B-ALL Microscopic Images Based on a Tiled Convolution Neural Network Approach
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    Chapter 12 Acute Lymphoblastic Leukemia Cells Image Analysis with Deep Bagging Ensemble Learning
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    Chapter 13 Leukemic B-Lymphoblast Cell Detection with Monte Carlo Dropout Ensemble Models
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    Chapter 14 ISBI Challenge 2019: Convolution Neural Networks for B-ALL Cell Classification
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    Chapter 15 Classification of Cancer Microscopic Images via Convolutional Neural Networks
Attention for Chapter 1: Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images
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Chapter title
Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images
Chapter number 1
Book title
ISBI 2019 C-NMC Challenge: Classification in Cancer Cell Imaging
Published by
Springer, Singapore, January 2019
DOI 10.1007/978-981-15-0798-4_1
Book ISBNs
978-9-81-150797-7, 978-9-81-150798-4
Authors

Atmika Honnalgere, Gaurav Nayak

Mendeley readers

Mendeley readers

The data shown below were compiled from readership statistics for 16 Mendeley readers of this research output. Click here to see the associated Mendeley record.

Geographical breakdown

Country Count As %
Unknown 16 100%

Demographic breakdown

Readers by professional status Count As %
Student > Bachelor 3 19%
Other 2 13%
Student > Doctoral Student 1 6%
Student > Master 1 6%
Researcher 1 6%
Other 0 0%
Unknown 8 50%
Readers by discipline Count As %
Computer Science 3 19%
Immunology and Microbiology 1 6%
Economics, Econometrics and Finance 1 6%
Neuroscience 1 6%
Materials Science 1 6%
Other 0 0%
Unknown 9 56%