‘Overly optimistic’ results
The researchers also investigated the potential risk of using pre-trained algorithms in a clinical setup, taking the algorithms that had been pre-trained on processed data and applying them to real-world raw data.
“The results were striking,” Shimron said. “The algorithms that had been adapted to processed data did poorly when they had to handle raw data.”
The images may look excellent, but they are inaccurate, the study authors said. “In some extreme cases, small, clinically important details related to pathology could be completely missing,” Shimron said.
While the algorithms might report crisper images and faster image acquisitions, the results cannot be reproduced with clinical, or raw scanner, data. These “overly optimistic” results reveal the risk of translating biased algorithms into clinical practice, the researchers said.
“No one can predict how these methods will work in clinical practice, and this creates a barrier to clinical adoption,” said Tamir, who earned his Ph.D. in electrical engineering and computer sciences at UC Berkeley and was a former member of Lustig’s lab. “It also makes it difficult to compare various competing methods, because some might be reporting performance on clinical data, while others might be reporting performance on processed data.”
Shimron said that revealing such “data crimes” is important since both industry and academia are rapidly working to develop new AI methods for medical imaging. She said that data curators could help by providing a full description on their website of the techniques used to process the files in their dataset. Additionally, the study offers specific guidelines to help MRI researchers design future studies without introducing these machine learning biases.
Funding from the National Institute of Biomedical Imaging and Bioengineering and the National Science Foundation Institute for Foundations of Machine Learning helped support this research.