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
GLOBETECH PUBLISHING LLC

Download Mobile App




Deep Learning Model Designed to Prevent Medical Imaging Cyberattacks

By MedImaging International staff writers
Posted on 18 Dec 2018
Print article
Image: New research presented at RSNA addressed the prevention of medical imaging cyberattacks (Photo courtesy of RSNA).
Image: New research presented at RSNA addressed the prevention of medical imaging cyberattacks (Photo courtesy of RSNA).
Researchers presented two new studies at the recent annual meeting of the Radiological Society of North America (RSNA) that addressed the potential risk of cyberattacks in medical imaging.

Medical imaging devices, such as X-ray, mammography, MRI and CT machines, play a crucial role in diagnosis and treatment. As these devices are typically connected to hospital networks, they can be potentially susceptible to sophisticated cyberattacks, including ransomware attacks that can disable the machines. Due to their critical role in the emergency room, CT devices may face the greatest risk of cyberattack. Researchers and cybersecurity experts have begun to examine ways to mitigate the risk of cyberattacks in medical imaging before they become a real danger.

In the first study presented at RSNA 2018, researchers from Ben-Gurion University of the Negev identified areas of vulnerability and ways to increase security in CT equipment. They demonstrated how a hacker could bypass security mechanisms of a CT machine to manipulate its behavior. Since CT uses ionizing radiation, changes to dose could negatively affect image quality, or in extreme cases even harm the patient. The researchers have developed a system for anomaly detection using various advanced machine learning and deep learning methods, with the training data consisting of actual commands recorded from real devices. The model learns to recognize normal commands and to predict if a new, unseen command is legitimate or not. If an attacker sends a malicious command to the device, the system will detect it and alert the operator before the command is executed.

"In the current phase of our research, we focus on developing solutions to prevent such attacks in order to protect medical devices," said Tom Mahler, Ph.D. candidate and teaching assistant at Ben-Gurion University of the Negev. "Our solution monitors the outgoing commands from the device before they are executed, and will alert—and possibly halt—if it detects anomalies."

"In cybersecurity, it is best to take the 'onion' model of protection and build the protection in layers," added Mahler. "Previous efforts in this area have focused on securing the hospital network. Our solution is device-oriented, and our goal is to be the last line of defense for medical imaging devices."

In the second study presented at this year’s RSNA, a team of Swiss researchers looked at the potential to tamper with mammogram results. The researchers trained a cycle-consistent generative adversarial network (CycleGAN), a type of artificial intelligence application, on 680 mammographic images from 334 patients, to convert images showing cancer to healthy ones and to do the same, in reverse, for the normal control images. Their aim was to determine if a CycleGAN could insert or remove cancer-specific features into mammograms in a realistic fashion. The images were presented to three radiologists, who reviewed the images and indicated whether they thought the images were genuine or modified. None of the radiologists could reliably distinguish between the two.

"As doctors, it is our moral duty to first protect our patients from harm," said Anton S. Becker, M.D, radiology resident at University Hospital Zurich and ETH Zurich, in Switzerland. "For example, as radiologists we are used to protecting patients from unnecessary radiation. When neural networks or other algorithms inevitably find their way into our clinical routine, we will need to learn how to protect our patients from any unwanted side effects of those as well."

Gold Member
Solid State Kv/Dose Multi-Sensor
AGMS-DM+
New
Thyroid Shield
Standard Thyroid Shield
New
Ultrasound Needle Guide
Ultra-Pro II
New
C-Arm with FPD
Digiscan V20 / V30

Print article

Channels

Radiography

view channel
:	Image: The AI model could be a valuable adjunct to human radiologists in breast cancer diagnoses and risk prediction (Photo courtesy of 123RF)

AI Model Predicts 5-Year Breast Cancer Risk from Mammograms

Approximately 13% of U.S. women, or one in every eight, are predicted to develop invasive breast cancer over their lifetime, with 1 in 39 women (3%) succumbing to the illness, according to the American... Read more

Nuclear Medicine

view channel
Image: The AI system uses scintigraphy imaging for early diagnosis of cardiac amyloidosis (Photo courtesy of 123RF)

AI System Automatically and Reliably Detects Cardiac Amyloidosis Using Scintigraphy Imaging

Cardiac amyloidosis, a condition characterized by the buildup of abnormal protein deposits (amyloids) in the heart muscle, severely affects heart function and can lead to heart failure or death without... Read more

General/Advanced Imaging

view channel
Image: The CIARTIC Move self-driving mobile C-arm has received FDA clearance (Photo courtesy of Siemens)

Self-Driving Mobile C-Arm Reduces Imaging Time during Surgery

Intraoperative imaging faces significant challenges due to staff shortages and the high demands placed on surgical teams in the operating room (OR). A common challenge during many OR procedures is the... 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 © 2000-2024 Globetech Media. All rights reserved.