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

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.

·  Front Matter

·  Classification of Normal Versus Malignant Cells in B-ALL White Blood Cancer Microscopic Images

·  Classification of Leukemic B-Lymphoblast Cells from Blood Smear Microscopic Images with an Attention-Based Deep Learning Method and Advanced Augmentation Techniques

·  Classification of Normal and Leukemic Blast Cells in B-ALL Cancer Using a Combination of Convolutional and Recurrent Neural Networks

·  Deep Learning for Classifying of White Blood Cancer

·  Ensemble Convolutional Neural Networks for Cell Classification in Microscopic Images

·  Acute Lymphoblastic Leukemia Classification from Microscopic Images Using Convolutional Neural Networks

·  Toward Automated Classification of B-Acute Lymphoblastic Leukemia

·  Neighborhood-Correction Algorithm for Classification of Normal and Malignant Cells

·  DeepMEN: Multi-model Ensemble Network for B-Lymphoblast Cell Classification

·  Multi-streams and Multi-features for Cell Classification

·  Classification of Normal Versus Malignant Cells in B-ALL Microscopic Images Based on a Tiled Convolution Neural Network Approach

·  Acute Lymphoblastic Leukemia Cells Image Analysis with Deep Bagging Ensemble Learning

·  Leukemic B-Lymphoblast Cell Detection with Monte Carlo Dropout Ensemble Models

·  ISBI Challenge 2019: Convolution Neural Networks for B-ALL Cell Classification

·         ·  Classification of Cancer Microscopic Images via Convolutional Neural Networks