12,352 research outputs found
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
ALEC: Active learning with ensemble of classifiers for clinical diagnosis of coronary artery disease
Invasive angiography is the reference standard for coronary artery disease (CAD) diagnosis but is expensive and
associated with certain risks. Machine learning (ML) using clinical and noninvasive imaging parameters can be
used for CAD diagnosis to avoid the side effects and cost of angiography. However, ML methods require labeled
samples for efficient training. The labeled data scarcity and high labeling costs can be mitigated by active
learning. This is achieved through selective query of challenging samples for labeling. To the best of our
knowledge, active learning has not been used for CAD diagnosis yet. An Active Learning with Ensemble of
Classifiers (ALEC) method is proposed for CAD diagnosis, consisting of four classifiers. Three of these classifiers
determine whether a patient’s three main coronary arteries are stenotic or not. The fourth classifier predicts
whether the patient has CAD or not. ALEC is first trained using labeled samples. For each unlabeled sample, if the
outputs of the classifiers are consistent, the sample along with its predicted label is added to the pool of labeled
samples. Inconsistent samples are manually labeled by medical experts before being added to the pool. The
training is performed once more using the samples labeled so far. The interleaved phases of labeling and training
are repeated until all samples are labeled. Compared with 19 other active learning algorithms, ALEC combined
with a support vector machine classifier attained superior performance with 97.01% accuracy. Our method is
justified mathematically as well. We also comprehensively analyze the CAD dataset used in this paper. As part of
dataset analysis, features pairwise correlation is computed. The top 15 features contributing to CAD and stenosis
of the three main coronary arteries are determined. The relationship between stenosis of the main arteries is
presented using conditional probabilities. The effect of considering the number of stenotic arteries on sample
discrimination is investigated. The discrimination power over dataset samples is visualized, assuming each of the
three main coronary arteries as a sample label and considering the two remaining arteries as sample features
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