Isbi 2019 C-Nmc Challenge: Classification in Cancer Cell Imaging: Select Proceedings

ISBN-13: 9789811508004 / Angielski / Miękka / 2021 / 147 str.

Anubha Gupta; Ritu Gupta

Spis treści

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: 

O autorze

Dr. Anubha Gupta received her PhD in Electrical Engineering from Indian Institute of Technology (IIT) Delhi in 2006. She completed her second Master’s as a full time student from the University of Maryland, College Park, USA from 2008-2010 in education with concentration: Higher Education Leadership and Policy Studies. She worked as Assistant Director with the Ministry of Information and Broadcasting, Government of India (through Indian Engineering Services) from 1993 to 1999 and as faculty for about 10 years before joining IIIT Delhi in December 2013, where she is currently working as Professor. She has author/co-authored over 80 technical papers in scientific journals and conferences. She has also filed 4 patents. She has published research papers in both engineering and education. Her research interests include biomedical signal and image processing including fMRI, MRI, DTI, EEG, ECG signal processing, genomics signal processing in cancer research, wavelets in deep learning, and signal processing for communication engineering. Dr. Gupta is a senior member of IEEE Signal processing Society, a member of IEEE Women in Engineering Society, and is Associate Editor of IEEE Access journal. 

Dr. Ritu Gupta is Professor of Laboratory Oncology at the All India Institute of Medical Sciences (AIIMS), New Delhi. She is currently spearheading the cancer laboratories at Dr. B.R. Ambedkar IRCH, AIIMS and is actively engaged in establishing diagnostic and research laboratories at the National Cancer Institute (NCI), Jhajjar, India. Dr. Gupta and her research group have established Unit of Excellence on Multiple Myeloma at AIIMS. Her lab is investigating the genomic and epigenomic alterations responsible for disease progression and treatment response in chronic lymphocytic leukemia and the molecular basis of disease heterogeneity in multiple myeloma. She is currently evaluating the prognostic and therapeutic implications of leukemic stem cells in acute myeloid leukemia. As a hematopathologist, she has a keen interest in digital processing of tumor cells and is working on image processing based software solutions for clinical applications. She has published more than 80 papers in peer reviewed journals and is actively engaged in academic activities at the national level for training of medical fraternity on advanced laboratory diagnostics including multi-parametric flow cytometry and molecular assays for clinical diagnostics and research.      

Blurb

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.