SERVO 07/08.2018 27
The future of transportation in waterway- rich cities such as Amsterdam, Bangkok,
and Venice — where canals run alongside and
under bustling streets and bridges — may
include autonomous boats that ferry goods
and people, helping to clear up road
Researchers from MIT’s Computer
Science and Artificial Intelligence Laboratory
(CSAIL) and the Senseable City Lab in the
Department of Urban Studies and Planning
(DUSP) have taken a step toward that future
by designing a fleet of autonomous boats that
offer high maneuverability and precise control.
The boats can also be rapidly 3D printed using
a low-cost printer, making mass manufacturing more feasible.
The boats could be used to taxi people around and to deliver goods,
easing street traffic. In the future, the researchers envision the driverless boats
being adapted to perform city services overnight, instead of during busy daylight
hours, further reducing congestion on both roads and canals.
AI CANCER DETECTION
Skin cancer detection won’t be turned over to robots anytime soon, but artificial intelligence detected skin
cancer more accurately than a large group of international
dermatologists in recent controlled testing, according to the
Agence France Presse.
In an academic study and clinical trial that pitted 58
dermatologists from 17 countries against a deep learning
convolutional neural network (CNN) and was published in
Annals of Oncology, the study’s lead author, Professor Holger
A. Haenssle, of the University of Heidelberg Department of
Dermatology, wrote, “Most dermatologists were
outperformed by the CNN. Regardless of any physician’s
Prior to the test, researchers from Germany, France, and
the US taught the CNN to differentiate benign skin lesions
from dangerous melanomas. In the process, the team showed
more than 100,000 images of correctly identified skin
cancers to the neural network, which was designed with
Google’s Inception v4 CNN architecture.
The 58 dermatologists were divided into three self-identified groups: beginners with less than two years of
experience; skilled with two to five years of experience; and
experts with more than five years of experience. There were
19 beginners, 11 skilled, and 30 experts among the group.
Two tests were run. In one test, the dermatologists were
shown 100 dermoscopic images with no other information.
They were asked to indicate whether the cancer was a
melanoma or benign. In addition, the doctors were asked
whether they would recommend excision, short-term follow-up, or no action. Four weeks later, the dermatologists were
shown the same images again; this time with additional
clinical information about the patients, plus close-up images.
The CNN scored higher than the overall group of
dermatologists on both tests. The dermatologists accurately
identified an average of 86.5 percent of the skin cancers on
the image-only test. In the second test, with more
information, the doctors averaged 88.9 percent accuracy. The
CNN, however, correctly detected the types of cancers 95
percent of the time based on images only.
Rated by experience, none of the three groups of
dermatologists was as accurate as the neural network. The
team did report, however, that 18 of the dermatologists
scored higher than the CNN.
“The CNN missed fewer melanomas, meaning it had a
higher sensitivity than the dermatologists,” Haenssle said. It
also “misdiagnosed fewer benign moles as malignant
melanoma ... this would result in less unnecessary surgery.”