“Our results call attention to a concept that has substantial practical implications, as computer vision and other machine learning algorithms begin to move from research to the clinical environment,” Pan said. “Namely, that the best results are likely to be achieved by combining multiple accurate and diverse models rather than from single models alone.”
Thus, practitioners aiming to incorporate machine learning algorithms into their workflow would benefit from having predictions obtained from different models, similar to how the accuracy of a radiological interpretation can be bolstered with multiple readers.
Ad Statistics
Times Displayed: 112448
Times Visited: 6718 MIT labs, experts in Multi-Vendor component level repair of: MRI Coils, RF amplifiers, Gradient Amplifiers Contrast Media Injectors. System repairs, sub-assembly repairs, component level repairs, refurbish/calibrate. info@mitlabsusa.com/+1 (305) 470-8013
Pan added that the findings also highlight the importance of open competitions like the 2017 RSNA Pediatric Bone Age Machine Learning Challenge, as they provide a standardized use case, a common training set, and an objective assessment method applied equally to all models.
“Machine learning competitions within radiology should be encouraged to spur development of heterogeneous models whose predictions can be combined to achieve optimal performance,” he said.
For the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge, researchers worked to develop algorithms that can identify and classify subtypes of hemorrhages on head CT scans. The data set, which comprises more than 25,000 head CT scans contributed by several research institutions, is the first multiplanar dataset used in an RSNA artificial intelligence challenge.
“Improving Automated Pediatric Bone Age Estimation Using Ensembles of Models from the 2017 RSNA Machine Learning Challenge.” Collaborating with Pan were Hans Henrik Thodberg, Ph.D., Safwan S. Halabi, M.D., Jayashree Kalpathy-Cramer, Ph.D., and David B. Larson, M.D., M.B.A.
Radiology: Artificial Intelligence is edited by Charles E. Kahn Jr., M.D., University of Pennsylvania (Penn) Perelman School of Medicine, Philadelphia, and owned and published by the Radiological Society of North America, Inc.
About RSNA
RSNA is an association of over 53,400 radiologists, radiation oncologists, medical physicists and related scientists promoting excellence in patient care and health care delivery through education, research and technologic innovation. The Society is based in Oak Brook, Ill.
Back to HCB News
