We use cookies to understand how you use our site and to improve your experience. This includes personalizing content and advertising. To learn more, click here. By continuing to use our site, you accept our use of cookies. Cookie Policy.

Features Partner Sites Information LinkXpress
Sign In
Advertise with Us

Download Mobile App




Events

ATTENTION: Due to the COVID-19 PANDEMIC, many events are being rescheduled for a later date, converted into virtual venues, or altogether cancelled. Please check with the event organizer or website prior to planning for any forthcoming event.

AI Algorithms Enable Highly Accurate and Cost Effective Medical Image Diagnosis

By MedImaging International staff writers
Posted on 13 Apr 2022
Print article
Image: New AI algorithms can enable highly accurate and cost effective medical image diagnostics (Photo courtesy of Pexels)
Image: New AI algorithms can enable highly accurate and cost effective medical image diagnostics (Photo courtesy of Pexels)

Medical imaging is an important part of modern healthcare, enhancing precision, reliability and development of treatment for various diseases. Artificial intelligence (AI) 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 labor intensive and time consuming.

Now, an engineering team at the University of Hong Kong (HKU, Hong Kong) 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.

For training REFERS, the research team used 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 only 100 radiographs that attained 83% accuracy in predictions. When the number was increased to 1,000, their model exhibited an amazing performance with an accuracy of 88.2%, which surpassed its counterpart trained with 10,000 radiologist annotations (accuracy of 87.6%). When 10,000 radiographs were used, the accuracy stood at 90.1%. In general, an accuracy level of 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.

"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 labeling.”

“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 Dr. Zhou Hong-Yu, the paper’s first author.

Related Links:
University of Hong Kong

Gold Supplier
Ultrasound Transducer/Probe Cleaner
Transeptic Cleaning Solution
New
Color Doppler Ultrasound Scanner
C50
New
Ultrasound System
Ultimus 9E
New
Radiography System
Riviera SPV

Print article

Channels

MRI

view channel
Image: New scan measures tumor oxygen levels in real-time to help guide treatment (Photo courtesy of ICR)

Oxygen-Enhanced MRI Technology Allows Cancer Doctors to See Inside Tumors

Since the 1950s, researchers have been aware of the difficulty in effectively treating tumors deprived of oxygen, a problem that is further exacerbated when treating them with radiotherapy.... Read more

Ultrasound

view channel
Image: New focused ultrasound is effective for treating Parkinson’s, movement disorders (Photo courtesy of Pexels)

New Focused Ultrasound Treatment Proves Effective for Parkinson’s Disease Patients

Parkinson's disease is a neurological condition characterized by the loss of dopamine neurons within the brain. While medications such as levodopa can be effective in managing this condition, some patients... Read more

Nuclear Medicine

view channel
Image: Tracking radiation treatment in real time promises safer, more effective cancer therapy (Photo courtesy of Pexels)

Real-Time 3D Imaging Provides First-of-Its-Kind View of X-Rays Hitting Inside Body During Radiation Therapy

Radiation is used in treatment for hundreds of thousands of cancer patients each year, bombarding an area of the body with high energy waves and particles, usually X-rays. The radiation can kill cancer... Read more

Imaging IT

view channel
Image: The new Medical Imaging Suite makes healthcare imaging data more accessible, interoperable and useful (Photo courtesy of Google Cloud)

New Google Cloud Medical Imaging Suite Makes Imaging Healthcare Data More Accessible

Medical imaging is a critical tool used to diagnose patients, and there are billions of medical images scanned globally each year. Imaging data accounts for about 90% of all healthcare data1 and, until... Read more
Copyright © 2000-2023 Globetech Media. All rights reserved.