Innovation i tech High

DIGITAL ECOSYSTEM
The Solution and the
Process
We have a clear vision:
to reinvent cost effective
disease management
through early detection
and intervention. We are
tackling this long term goal
by making cancer screening
and diagnostics as accurate
and accessible as possible,
by using advanced analytics
tools like image processing
and deep learning, Natural
Language Processing (NLP)
to improve patient care,
hospital administration,
supply chain and logistic
efficiencies.
The process is to first digitize
the sample slides through
a microscope, scan whole
slides and upload onto the
cloud by converting the large
image into a binary metadata.
AI engine then takes over,
which classify and tag the
visual data, not only to churn
out results, but also support
it with visual evidence (like
tagging & annotations) to
make it easily verifiable.
With the advancement of
AI, as more and more new
data keeps coming in, the
machine continues to learn
itself like humans. And it
starts generating solutions.
There is no need for a manual
review by a pathologist
by putting a slide under
a microscope. Remote
diagnosis is made possible,
because the pathologist
need not be sitting next to
the microscope or blood
slide. They can access from
anywhere, anytime.
Our method is primarily
based on a convolutional
neural network (CNN) and
the Wide Res Net 50 residual
network formulation. In our
approach, we first trained the
CNNs on a suitable set of test
datasets. Then we applied
the trained deep model to
partially overlapping patches
from each whole slide image
(WSI) to create prediction
heatmaps.
By utilising machine learning
on whole slide images
(WSI), we have developed
a prototype for automatic
detection of metastasis in a
lymph node. Our early results
of detection were shown to
have an accuracy of upto 73
percent.
Our deep learning expert team
and a pathologist is needed
to work closely to translate the
deep learning algorithms to
digital pathology tasks.
It is not that AI will entirely
replace histopathologists, but
rather it will provide increased
efficiency and accuracy to
the diagnosis, by providing a
preliminary diagnosis much
faster.
About the Author
Khalid Shaikh is the Founder,
CEO of Prognica and Affaan
Technologies. An Entrepreneur,
a Technocrat and a Business
Strategist, he is passionate
about disruptive ideas that
transform the human experience
and create meaningful lasting
change. Parallel to this, he
has been involved in few tech
startups as an advisor and
mentor.
50 www.innovationandtech.ae
INNOVATIONANDTECH
January | 2018
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