INDUSTRY WATCH
MANUAL CLASSIFICATION
OF PAP SMEARS FOR
CERVICAL CANCER
DETECTION IS A
LABORIOUS AND TIME-
CONSUMING PROCESS.
E
fficiency in the healthcare sector
has been increased through
the use of pattern recognition
and improved applications of neural
processing. Perhaps most significantly it
has been utilised in the early detection
of cervical cancer. This is one area where
the use of Artificial Intelligence (AI) can
prove especially beneficial as it avoids the
laborious and time-consuming process
of manually classifying pap smears for
cervical cancer detection.
But the use of such technology even
has the potential to detect and avoid
an epidemic of disease across multiple
continents or even worldwide. The story
of the application of AI in the healthcare
sector is underway but we are surely
only at the tip of the iceberg. Here we
ask Pierre Brunswick, CEO, NeuroMem,
questions about its true potential.
Can you explain how your
solutions have been utilised in the
early detection of cervical cancer?
Artificial intelligence (AI) is helping the
healthcare sector to increase efficiency
through the use of pattern recognition
and improved applications of neural
processing locally vis-à-vis moving data
offsite to a cloud server. One of the
earliest applications was development
by Dr Manan Suri, Assistant Professor
with the Department of Electrical
Engineering, Indian Institute of
Technology-Delhi (IIT-Delhi), who has
been using NeuroMem technology to
develop proof of concept neuromorphic/
AI applications in fields such as
healthcare (to augment diagnosis of
www.intelligentcio.com
cervical cancer from pap smears tests
available publicly).
Dr Suri and his team worked on an ultra-
fast, efficient, low-power, neuromorphic
hardware-based solution for cervical
cancer diagnosis. Manual classification of
pap smears for cervical cancer detection is
a laborious and time-consuming process.
They trained a bio-inspired, dedicated
ASIC on parameters of cell nuclei
of ground truth images for normal/
abnormal cell classification. They have
been able to validate their approach on
both single and multi-cell images.
Given the number of cervical cancer
suspect cases and the difference that
a timely diagnosis can make, there
is a strong need for development of
intelligent, low-cost, portable, low-power
preliminary diagnostic hardware.
Progress in the field of high-performance
imaging hardware for smartphones
has created a favourable pre-cursor for
development of true portable mobile
on-the-fly diagnostic systems. Bio-inspired
neuromorphic hardware or artificial neural
networks (ANNs) in hardware have proven
to be promising in the field of image
processing, speech recognition, pattern
recognition and medical diagnostics.
Can you explain how the system is
used to detect anomalies?
At NeuroMem, we truly believe that
nonstop learning, and pattern recognition
offered by our technology can become
practical and ubiquitous only if it can rely
INTELLIGENTCIO
75