1,841 research outputs found
Exploring to establish an appropriate model for image aesthetic assessment via CNN-based RSRL: An empirical study
To establish an appropriate model for photo aesthetic assessment, in this
paper, a D-measure which reflects the disentanglement degree of the final layer
FC nodes of CNN is introduced. By combining F-measure with D-measure to obtain
a FD measure, an algorithm of determining the optimal model from the multiple
photo score prediction models generated by CNN-based repetitively self-revised
learning(RSRL) is proposed. Furthermore, the first fixation perspective(FFP)
and the assessment interest region(AIR) of the models are defined and
calculated. The experimental results show that the FD measure is effective for
establishing the appropriate model from the multiple score prediction models
with different CNN structures. Moreover, the FD-determined optimal models with
the comparatively high FD always have the FFP an AIR which are close to the
human's aesthetic perception when enjoying photos
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
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