2,442 research outputs found
A generic ensemble based deep convolutional neural network for semi-supervised medical image segmentation
Deep learning based image segmentation has achieved the state-of-the-art
performance in many medical applications such as lesion quantification, organ
detection, etc. However, most of the methods rely on supervised learning, which
require a large set of high-quality labeled data. Data annotation is generally
an extremely time-consuming process. To address this problem, we propose a
generic semi-supervised learning framework for image segmentation based on a
deep convolutional neural network (DCNN). An encoder-decoder based DCNN is
initially trained using a few annotated training samples. This initially
trained model is then copied into sub-models and improved iteratively using
random subsets of unlabeled data with pseudo labels generated from models
trained in the previous iteration. The number of sub-models is gradually
decreased to one in the final iteration. We evaluate the proposed method on a
public grand-challenge dataset for skin lesion segmentation. Our method is able
to significantly improve beyond fully supervised model learning by
incorporating unlabeled data.Comment: Accepted for publication at IEEE International Symposium on
Biomedical Imaging (ISBI) 202
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Deep learning for cardiac image segmentation: A review
Deep learning has become the most widely used approach for cardiac image segmentation in recent years. In this paper, we provide a review of over 100 cardiac image segmentation papers using deep learning, which covers common imaging modalities including magnetic resonance imaging (MRI), computed tomography (CT), and ultrasound (US) and major anatomical structures of interest (ventricles, atria and vessels). In addition, a summary of publicly available cardiac image datasets and code repositories are included to provide a base for encouraging reproducible research. Finally, we discuss the challenges and limitations with current deep learning-based approaches (scarcity of labels, model generalizability across different domains, interpretability) and suggest potential directions for future research
Deep Learning in Cardiology
The medical field is creating large amount of data that physicians are unable
to decipher and use efficiently. Moreover, rule-based expert systems are
inefficient in solving complicated medical tasks or for creating insights using
big data. Deep learning has emerged as a more accurate and effective technology
in a wide range of medical problems such as diagnosis, prediction and
intervention. Deep learning is a representation learning method that consists
of layers that transform the data non-linearly, thus, revealing hierarchical
relationships and structures. In this review we survey deep learning
application papers that use structured data, signal and imaging modalities from
cardiology. We discuss the advantages and limitations of applying deep learning
in cardiology that also apply in medicine in general, while proposing certain
directions as the most viable for clinical use.Comment: 27 pages, 2 figures, 10 table
A Survey on Deep Learning in Medical Image Analysis
Deep learning algorithms, in particular convolutional networks, have rapidly
become a methodology of choice for analyzing medical images. This paper reviews
the major deep learning concepts pertinent to medical image analysis and
summarizes over 300 contributions to the field, most of which appeared in the
last year. We survey the use of deep learning for image classification, object
detection, segmentation, registration, and other tasks and provide concise
overviews of studies per application area. Open challenges and directions for
future research are discussed.Comment: Revised survey includes expanded discussion section and reworked
introductory section on common deep architectures. Added missed papers from
before Feb 1st 201
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Pattern classification approaches for breast cancer identification via MRI: stateāofātheāart and vision for the future
Mining algorithms for Dynamic Contrast Enhanced Magnetic Resonance Imaging (DCEMRI)
of breast tissue are discussed. The algorithms are based on recent advances in multidimensional
signal processing and aim to advance current stateāofātheāart computerāaided detection
and analysis of breast tumours when these are observed at various states of development. The topics
discussed include image feature extraction, information fusion using radiomics, multiāparametric
computerāaided classification and diagnosis using information fusion of tensorial datasets as well
as Clifford algebra based classification approaches and convolutional neural network deep learning
methodologies. The discussion also extends to semiāsupervised deep learning and selfāsupervised
strategies as well as generative adversarial networks and algorithms using generated
confrontational learning approaches. In order to address the problem of weakly labelled tumour
images, generative adversarial deep learning strategies are considered for the classification of
different tumour types. The proposed data fusion approaches provide a novel Artificial Intelligence
(AI) based framework for more robust image registration that can potentially advance the early
identification of heterogeneous tumour types, even when the associated imaged organs are
registered as separate entities embedded in more complex geometric spaces. Finally, the general
structure of a highādimensional medical imaging analysis platform that is based on multiātask
detection and learning is proposed as a way forward. The proposed algorithm makes use of novel
loss functions that form the building blocks for a generated confrontation learning methodology
that can be used for tensorial DCEāMRI. Since some of the approaches discussed are also based on
timeālapse imaging, conclusions on the rate of proliferation of the disease can be made possible. The
proposed framework can potentially reduce the costs associated with the interpretation of medical
images by providing automated, faster and more consistent diagnosis
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