Page 8 - Handbook of Deep Learning in Biomedical Engineering Techniques and Applications

Preface

Deep learning has been rapidly developed in the recent years,
in terms of both methodological development and practical ap-
plications. It provides computational models of multiple pro-
cessing layers to learn and represent data with multiple levels of
abstraction. It is able to implicitly capture intricate structures of
large-scale data and is ideally suited to some of the hardware
architectures that are currently available. The purpose of this
book is to provide a diverse, but complementary, set of contri-
butions to demonstrate new developments and applications of
deep learning and computational machine learning to solve
problems in biomedical engineering. The proposed book will be
organized as a reference source for enabling readers to have an
idea about the relation between deep learning and biomedical
engineering.
In Chapter 1, survey of deep learning is used for image clas-
sification, carotid ultrasound data investigation, cardiotocog-
raphy, intravascular ultrasound report, lung CT report, brain
tumor prediction, object detection, segmentation, breast cancer
prediction, ECG (electrocardiogram) signal, EEG (electroen-
cephalogram), PPG signal registration, and psoriasis skin disease
as well as cancer detection. Concise summaries are delivered of
trainings per application zone: pulmonary, musculoskeletal
neuro, digital pathology, and abdominal, retinal, breast, and
cardiac. There are various types of deep learning techniques
present to improve accuracy of the medical dataset.
Chapter 2 provides a detailed discussion on different
convolutional neural network (CNN) architectures and their
applications in the medical imaging domain. Moreover, a state-
of-the-art comparison has been carried out between several
existing works inside medical imaging based on CNN. Lastly, the
work concludes with several critical remarks highlighting future
challenges and their solutions.
Chapter 3 discusses about the class of tools that enable deep
learning engineers to actually do their work faster and more
effectively. Some of the tools include TensorFlow, Keras, Caffe,
and Torch. Deep learning models make use of several kinds of