FM JANUARY 2019 - digital edition

for training. In both learning methods,
large datasets of image features are
automatically extracted from each
data point/image. By using these
approaches of machine learning —
along with statistical tools like logistic
regression, support vector machine and
decision trees — a better, feature-based
separation between normal and disease
conditions are achieved (Cortes C,
Vapnik V. Mach Learn 1995).
In radiology, the data-driven
approaches work by using specific
features designed to reflect the
properties of data, such as density,
heterogeneity of tumours, shape etc.
Newer approaches are being developed
using deep learning (Chartrand G,
et al, Radiographics 2017), which
are improving the feature-based
methods by using artificial neural
networks (ANNs). These ANNs work by
introducing a hierarchy of non-linear,
multi-layer data nodes including the
pixel values in an image. Thousands of
these nodes with millions of networks
become the best way of training the
algorithms to respond to the new
inputs for diagnostics. This approach
moves ways from a hypothesis-based
approach to a data-driven model, which
is more powerful and leads to novel
discoveries. The first sets of features,
called engineered features, are specific
characteristics of disease tissues which
are used by domain-specific experts. In
case of scarcity of data, a pre-trained
network can be used to perform
transfer learning. For any deep learning
approach, data normalisation is
an essential preprocessing
step. This ensures better
numerical stability
and quicker
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