|
Showing 1 - 9 of
9 matches in All Departments
This book reviews the state of the art in deep learning approaches
to high-performance robust disease detection, robust and accurate
organ segmentation in medical image computing (radiological and
pathological imaging modalities), and the construction and mining
of large-scale radiology databases. It particularly focuses on the
application of convolutional neural networks, and on recurrent
neural networks like LSTM, using numerous practical examples to
complement the theory. The book's chief features are as follows: It
highlights how deep neural networks can be used to address new
questions and protocols, and to tackle current challenges in
medical image computing; presents a comprehensive review of the
latest research and literature; and describes a range of different
methods that employ deep learning for object or landmark detection
tasks in 2D and 3D medical imaging. In addition, the book examines
a broad selection of techniques for semantic segmentation using
deep learning principles in medical imaging; introduces a novel
approach to text and image deep embedding for a large-scale chest
x-ray image database; and discusses how deep learning relational
graphs can be used to organize a sizable collection of radiology
findings from real clinical practice, allowing semantic
similarity-based retrieval.The intended reader of this edited book
is a professional engineer, scientist or a graduate student who is
able to comprehend general concepts of image processing, computer
vision and medical image analysis. They can apply computer science
and mathematical principles into problem solving practices. It may
be necessary to have a certain level of familiarity with a number
of more advanced subjects: image formation and enhancement, image
understanding, visual recognition in medical applications,
statistical learning, deep neural networks, structured prediction
and image segmentation.
Most of the modern machine learning models, based on deep learning
techniques, depend on carefully curated and cleanly labelled
training sets to be reliably trained and deployed. However, the
expensive labelling process involved in the acquisition of such
training sets limits the number and size of datasets available to
build new models, slowing down progress in the field.
Alternatively, many poorly curated training sets containing noisy
labels are readily available to be used to build new models.
However, the successful exploration of such noisy-label training
sets depends on the development of algorithms and models that are
robust to these noisy labels. Machine learning and Noisy Labels:
Definitions, Theory, Techniques and Solutions defines different
types of label noise, introduces the theory behind the problem,
presents the main techniques that enable the effective use of
noisy-label training sets, and explains the most accurate methods
developed in the field. This book is an ideal introduction to
machine learning with noisy labels suitable for senior
undergraduates, post graduate students, researchers and
practitioners using, and researching into, machine learning
methods.
This LNCS workshop proceedings, ACCV 2018, contains carefully
reviewed and selected papers from 11 workshops, each having
different types or programs: Scene Understanding and Modelling
(SUMO) Challenge, Learning and Inference Methods for High
Performance Imaging (LIMHPI), Attention/Intention Understanding
(AIU), Museum Exhibit Identification Challenge (Open MIC) for
Domain Adaptation and Few-Shot Learning, RGB-D - Sensing and
Understanding via Combined Colour and Depth, Dense 3D
Reconstruction for Dynamic Scenes, AI Aesthetics in Art and Media
(AIAM), Robust Reading (IWRR), Artificial Intelligence for Retinal
Image Analysis (AIRIA), Combining Vision and Language, Advanced
Machine Vision for Real-life and Industrially Relevant Applications
(AMV).
|
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 4th International Workshop, DLMIA 2018, and 8th International Workshop, ML-CDS 2018, Held in Conjunction with MICCAI 2018, Granada, Spain, September 20, 2018, Proceedings (Paperback, 1st ed. 2018)
Danail Stoyanov, Zeike Taylor, Gustavo Carneiro, Tanveer Syeda-Mahmood, Anne Martel, …
|
R1,593
Discovery Miles 15 930
|
Ships in 10 - 15 working days
|
This book constitutes the refereed joint proceedings of the 4th
International Workshop on Deep Learning in Medical Image Analysis,
DLMIA 2018, and the 8th International Workshop on Multimodal
Learning for Clinical Decision Support, ML-CDS 2018, held in
conjunction with the 21st International Conference on Medical
Imaging and Computer-Assisted Intervention, MICCAI 2018, in
Granada, Spain, in September 2018. The 39 full papers presented at
DLMIA 2018 and the 4 full papers presented at ML-CDS 2018 were
carefully reviewed and selected from 85 submissions to DLMIA and 6
submissions to ML-CDS. The DLMIA papers focus on the design and use
of deep learning methods in medical imaging. The ML-CDS papers
discuss new techniques of multimodal mining/retrieval and their use
in clinical decision support.
|
Deep Learning and Data Labeling for Medical Applications - First International Workshop, LABELS 2016, and Second International Workshop, DLMIA 2016, Held in Conjunction with MICCAI 2016, Athens, Greece, October 21, 2016, Proceedings (Paperback, 1st ed. 2016)
Gustavo Carneiro, Diana Mateus, Loic Peter, Andrew Bradley, Joao Manuel R.S. Tavares, …
|
R2,418
Discovery Miles 24 180
|
Ships in 10 - 15 working days
|
This book constitutes the refereed proceedings of two workshops
held at the 19th International Conference on Medical Image
Computing and Computer-Assisted Intervention, MICCAI 2016, in
Athens, Greece, in October 2016: the First Workshop on Large-Scale
Annotation of Biomedical Data and Expert Label Synthesis, LABELS
2016, and the Second International Workshop on Deep Learning in
Medical Image Analysis, DLMIA 2016. The 28 revised regular papers
presented in this book were carefully reviewed and selected from a
total of 52 submissions. The 7 papers selected for LABELS deal with
topics from the following fields: crowd-sourcing methods; active
learning; transfer learning; semi-supervised learning; and modeling
of label uncertainty.The 21 papers selected for DLMIA span a wide
range of topics such as image description; medical imaging-based
diagnosis; medical signal-based diagnosis; medical image
reconstruction and model selection using deep learning techniques;
meta-heuristic techniques for fine-tuning parameter in deep
learning-based architectures; and applications based on deep
learning techniques.
This book reviews the state of the art in deep learning approaches
to high-performance robust disease detection, robust and accurate
organ segmentation in medical image computing (radiological and
pathological imaging modalities), and the construction and mining
of large-scale radiology databases. It particularly focuses on the
application of convolutional neural networks, and on recurrent
neural networks like LSTM, using numerous practical examples to
complement the theory. The book's chief features are as follows: It
highlights how deep neural networks can be used to address new
questions and protocols, and to tackle current challenges in
medical image computing; presents a comprehensive review of the
latest research and literature; and describes a range of different
methods that employ deep learning for object or landmark detection
tasks in 2D and 3D medical imaging. In addition, the book examines
a broad selection of techniques for semantic segmentation using
deep learning principles in medical imaging; introduces a novel
approach to text and image deep embedding for a large-scale chest
x-ray image database; and discusses how deep learning relational
graphs can be used to organize a sizable collection of radiology
findings from real clinical practice, allowing semantic
similarity-based retrieval.The intended reader of this edited book
is a professional engineer, scientist or a graduate student who is
able to comprehend general concepts of image processing, computer
vision and medical image analysis. They can apply computer science
and mathematical principles into problem solving practices. It may
be necessary to have a certain level of familiarity with a number
of more advanced subjects: image formation and enhancement, image
understanding, visual recognition in medical applications,
statistical learning, deep neural networks, structured prediction
and image segmentation.
|
Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - Third International Workshop, DLMIA 2017, and 7th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017, Quebec City, QC, Canada, September 14, Proceedings (Paperback, 1st ed. 2017)
M. Jorge Cardoso, Tal Arbel, Gustavo Carneiro, Tanveer Syeda-Mahmood, Joao Manuel R.S. Tavares, …
|
R3,146
Discovery Miles 31 460
|
Ships in 10 - 15 working days
|
This book constitutes the refereed joint proceedings of the Third
International Workshop on Deep Learning in Medical Image Analysis,
DLMIA 2017, and the 6th International Workshop on Multimodal
Learning for Clinical Decision Support, ML-CDS 2017, held in
conjunction with the 20th International Conference on Medical
Imaging and Computer-Assisted Intervention, MICCAI 2017, in Quebec
City, QC, Canada, in September 2017. The 38 full papers presented
at DLMIA 2017 and the 5 full papers presented at ML-CDS 2017 were
carefully reviewed and selected. The DLMIA papers focus on the
design and use of deep learning methods in medical imaging. The
ML-CDS papers discuss new techniques of multimodal mining/retrieval
and their use in clinical decision support.
For robots to navigate and interact more richly with the world
around them, they will likely require a deeper understanding of the
world in which they operate. In robotics and related research
fields, the study of understanding is often referred to as
semantics, which dictates what does the world 'mean' to a robot,
and is strongly tied to the question of how to represent that
meaning. With humans and robots increasingly operating in the same
world, the prospects of human-robot interaction also bring
semantics and ontology of natural language into the picture. Driven
by need, as well as by enablers like increasing availability of
training data and computational resources, semantics is a rapidly
growing research area in robotics. The field has received
significant attention in the research literature to date, but most
reviews and surveys have focused on particular aspects of the
topic: the technical research issues regarding its use in specific
robotic topics like mapping or segmentation, or its relevance to
one particular application domain like autonomous driving. A new
treatment is therefore required, and is also timely because so much
relevant research has occurred since many of the key surveys were
published. This survey provides an overarching snapshot of where
semantics in robotics stands today. We establish a taxonomy for
semantics research in or relevant to robotics, split into four
broad categories of activity in which semantics are extracted,
used, or both. Within these broad categories, we survey dozens of
major topics including fundamentals from the computer vision field
and key robotics research areas utilizing semantics such as
mapping, navigation and interaction with the world. The survey also
covers key practical considerations, including enablers like
increased data availability and improved computational hardware,
and major application areas where semantics is or is likely to play
a key role. In creating this survey, we hope to provide researchers
across academia and industry with a comprehensive reference that
helps facilitate future research in this exciting field.
This book presents a detailed review of the state of the art in
deep learning approaches for semantic object detection and
segmentation in medical image computing, and large-scale radiology
database mining. A particular focus is placed on the application of
convolutional neural networks, with the theory supported by
practical examples. Features: highlights how the use of deep neural
networks can address new questions and protocols, as well as
improve upon existing challenges in medical image computing;
discusses the insightful research experience of Dr. Ronald M.
Summers; presents a comprehensive review of the latest research and
literature; describes a range of different methods that make use of
deep learning for object or landmark detection tasks in 2D and 3D
medical imaging; examines a varied selection of techniques for
semantic segmentation using deep learning principles in medical
imaging; introduces a novel approach to interleaved text and image
deep mining on a large-scale radiology image database.
|
You may like...
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
Loot
Nadine Gordimer
Paperback
(2)
R398
R330
Discovery Miles 3 300
|