3,803 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
Finding Streams in Knowledge Graphs to Support Fact Checking
The volume and velocity of information that gets generated online limits
current journalistic practices to fact-check claims at the same rate.
Computational approaches for fact checking may be the key to help mitigate the
risks of massive misinformation spread. Such approaches can be designed to not
only be scalable and effective at assessing veracity of dubious claims, but
also to boost a human fact checker's productivity by surfacing relevant facts
and patterns to aid their analysis. To this end, we present a novel,
unsupervised network-flow based approach to determine the truthfulness of a
statement of fact expressed in the form of a (subject, predicate, object)
triple. We view a knowledge graph of background information about real-world
entities as a flow network, and knowledge as a fluid, abstract commodity. We
show that computational fact checking of such a triple then amounts to finding
a "knowledge stream" that emanates from the subject node and flows toward the
object node through paths connecting them. Evaluation on a range of real-world
and hand-crafted datasets of facts related to entertainment, business, sports,
geography and more reveals that this network-flow model can be very effective
in discerning true statements from false ones, outperforming existing
algorithms on many test cases. Moreover, the model is expressive in its ability
to automatically discover several useful path patterns and surface relevant
facts that may help a human fact checker corroborate or refute a claim.Comment: Extended version of the paper in proceedings of ICDM 201
Deep learning in remote sensing: a review
Standing at the paradigm shift towards data-intensive science, machine
learning techniques are becoming increasingly important. In particular, as a
major breakthrough in the field, deep learning has proven as an extremely
powerful tool in many fields. Shall we embrace deep learning as the key to all?
Or, should we resist a 'black-box' solution? There are controversial opinions
in the remote sensing community. In this article, we analyze the challenges of
using deep learning for remote sensing data analysis, review the recent
advances, and provide resources to make deep learning in remote sensing
ridiculously simple to start with. More importantly, we advocate remote sensing
scientists to bring their expertise into deep learning, and use it as an
implicit general model to tackle unprecedented large-scale influential
challenges, such as climate change and urbanization.Comment: Accepted for publication IEEE Geoscience and Remote Sensing Magazin
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