Engineering team develops new AI algorithms for high accuracy and cost effective medical image diagnostics
Date:
April 12, 2022
Source:
The University of Hong Kong
Summary:
Medical imaging is an important part of modern healthcare, enhancing
both the precision, reliability and development of treatment for
various diseases. Artificial intelligence has also been widely
used to further enhance the process. However, conventional medical
image diagnosis employing AI algorithms require large amounts of
annotations as supervision signals for model training. To acquire
accurate labels for the AI algorithms -- radiologists, as part of
the clinical routine, prepare radiology reports for each of their
patients, followed by annotation staff extracting and confirming
structured labels from those reports using human-defined rules
and existing natural language processing (NLP) tools. The ultimate
accuracy of extracted labels hinges on the quality of human work
and various NLP tools. The method comes at a heavy price, being
both labour intensive and time consuming. An engineering team has
now developed a new approach which can cut human cost down by 90%,
by enabling the automatic acquisition of supervision signals from
hundreds of thousands of radiology reports at the same time.
It attains a high accuracy in predictions, surpassing its
counterpart of conventional medical image diagnosis employing
AI algorithms.
FULL STORY ========================================================================== Medical imaging is an important part of modern healthcare, enhancing
both the precision, reliability and development of treatment for various diseases.
Artificial intelligence has also been widely used to further enhance
the process.
========================================================================== However, conventional medical image diagnosis employing AI algorithms
require large amounts of annotations as supervision signals for
model training. To acquire accurate labels for the AI algorithms -- radiologists, as part of the clinical routine, prepare radiology reports
for each of their patients, followed by annotation staff extracting
and confirming structured labels from those reports using human-defined
rules and existing natural language processing (NLP) tools. The ultimate accuracy of extracted labels hinges on the quality of human work and
various NLP tools. The method comes at a heavy price, being both labour intensive and time consuming.
An engineering team at the University of Hong Kong (HKU) has developed a
new approach "REFERS" (Reviewing Free-text Reports for Supervision), which
can cut human cost down by 90%, by enabling the automatic acquisition
of supervision signals from hundreds of thousands of radiology reports
at the same time. It attains a high accuracy in predictions, surpassing
its counterpart of conventional medical image diagnosis employing AI algorithms.
The innovative approach marks a solid step towards realizing generalized medical artificial intelligence. The breakthrough was published in
Nature Machine Intelligence in the paper titled "Generalized radiograph representation learning via cross-supervision between images and free-text radiology reports." "AI-enabled medical image diagnosis has the potential
to support medical specialists in reducing their workload and improving
the diagnostic efficiency and accuracy, including but not limited to
reducing the diagnosis time and detecting subtle disease patterns,"
said Professor YU Yizhou, leader of the team from HKU's Department of
Computer Science under the Faculty of Engineering.
"We believe abstract and complex logical reasoning sentences in radiology reports provide sufficient information for learning easily transferable
visual features. With appropriate training, REFERS directly learns
radiograph representations from free-text reports without the need to
involve manpower in labelling." Professor Yu remarked.
For training REFERS, the research team uses a public database with
370,000 X- Ray images, and associated radiology reports, on 14 common
chest diseases including atelectasis, cardiomegaly, pleural effusion,
pneumonia and pneumothorax. The researchers managed to build a radiograph recognition model using 100 radiographs only, and attains 83% accuracy
in predictions. When the number was increased to 1,000, their model
exhibits amazing performance with an accuracy of 88.2%, which surpasses
its counterpart trained with 10,000 radiologist annotations (accuracy at 87.6%). When 10,000 radiographs were used, the accuracy is at 90.1%. In general, an accuracy above 85% in predictions is useful in real-world
clinical applications.
REFERS achieves the goal by accomplishing two report-related tasks,
i.e., report generation and radiograph-report matching. In the first
task, REFERS translates radiographs into text reports by first encoding radiographs into an intermediate representation, which is then used to
predict text reports via a decoder network. A cost function is defined
to measure the similarity between predicted and real report texts,
based on which gradient-based optimization is employed to train the
neural network and update its weights.
As for the second task, REFERS first encodes both radiographs and
free-text reports into the same semantic space, where representations of
each report and its associated radiographs are aligned via contrastive learning.
"Compared to conventional methods that heavily rely on human
annotations, REFERS has the ability to acquire supervision from each
word in the radiology reports. We can substantially reduce the amount
of data annotation by 90% and the cost to build medical artificial intelligence. It marks a significant step towards realizing generalized
medical artificial intelligence, " said the paper's first author Dr
ZHOU Hong-Yu.
========================================================================== Story Source: Materials provided by The_University_of_Hong_Kong. Note:
Content may be edited for style and length.
========================================================================== Journal Reference:
1. Hong-Yu Zhou, Xiaoyu Chen, Yinghao Zhang, Ruibang Luo, Liansheng
Wang,
Yizhou Yu. Generalized radiograph representation learning via
cross- supervision between images and free-text radiology
reports. Nature Machine Intelligence, 2022; 4 (1): 32 DOI:
10.1038/s42256-021-00425-9 ==========================================================================
Link to news story:
https://www.sciencedaily.com/releases/2022/04/220412095357.htm
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