Sixty to seventy million people in the U.S. suffer from gastrointestinal diseases and the best way to clinically diagnose the exact problem is to perform an abdominal ultrasound. However, the process is labor intensive and sometimes inefficient. To help solve the issue, researchers from Siemens and Vanderbilt University developed a deep learning-based system that can automatically interpret abdominal ultrasound images and detect organs and abnormalities.
The researchers say this is the first deep learning system that uses an integrated system to classify abdominal ultrasounds automatically.
“Automatic view classification and landmark detection of the abdominal organs on ultrasound images can be instrumental to streamline the examination workflow,” the researchers wrote in their research paper. “We pursue a highly integrated multi-task learning framework to perform simultaneous view classification and landmark detection automatically to increase the efficiency of abdominal ultrasound examination workflow.”
“While convolutional neural networks (CNN) have demonstrated more promising outcomes on ultrasound image analytics than traditional machine learning approaches, it becomes impractical to deploy multiple networks (one for each task) due to the limited computational and memory resources on most existing ultrasound scanners,” the team said. “To overcome such limits, we propose a multitask learning framework to handle all the tasks by a single network.”
The neural network can perform view classification and landmark detection simultaneously.
According to the researchers, the method outperforms the approaches that address each task individually.
The paper was published on ArXiv on Monday.