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posted by Fnord666 on Wednesday May 13 2020, @02:12PM   Printer-friendly
from the TANSTAAFL dept.

AI techniques in medical imaging may lead to incorrect diagnoses:

A team of researchers, led by the University of Cambridge and Simon Fraser University, designed a series of tests for medical image reconstruction algorithms based on AI and deep learning, and found that these techniques result in myriad artefacts, or unwanted alterations in the data, among other major errors in the final images. The effects were typically not present in non-AI based imaging techniques.

The phenomenon was widespread across different types of artificial neural networks, suggesting that the problem will not be easily remedied. The researchers caution that relying on AI-based image reconstruction techniques to make diagnoses and determine treatment could ultimately do harm to patients. Their results are reported in the Proceedings of the National Academy of Sciences.

"There's been a lot of enthusiasm about AI in medical imaging, and it may well have the potential to revolutionise modern medicine: however, there are potential pitfalls that must not be ignored," said Dr Anders Hansen from Cambridge's Department of Applied Mathematics and Theoretical Physics, who led the research with Dr Ben Adcock from Simon Fraser University. "We've found that AI techniques are highly unstable in medical imaging, so that small changes in the input may result in big changes in the output."

A typical MRI scan can take anywhere between 15 minutes and two hours, depending on the size of the area being scanned and the number of images being taken. The longer the patient spends inside the machine, the higher resolution the final image will be. However, limiting the amount of time patients spend inside the machine is desired, both to reduce the risk to individual patients and to increase the overall number of scans that can be performed.

Using AI techniques to improve the quality of images from MRI scans or other types of medical imaging is an attractive possibility for solving the problem of getting the highest quality image in the smallest amount of time: in theory, AI could take a low-resolution image and make it into a high-resolution version. AI algorithms 'learn' to reconstruct images based on training from previous data, and through this training procedure aim to optimise the quality of the reconstruction. This represents a radical change compared to classical reconstruction techniques that are solely based on mathematical theory without dependency on previous data. In particular, classical techniques do not learn.

[...] The researchers are now focusing on providing the fundamental limits to what can be done with AI techniques. Only when these limits are known will we be able to understand which problems can be solved. "Trial and error-based research would never discover that the alchemists could not make gold: we are in a similar situation with modern AI," said Hansen. "These techniques will never discover their own limitations. Such limitations can only be shown mathematically."

Journal Reference
Vegard Antun, Francesco Renna, Clarice Poon, et al. On instabilities of deep learning in image reconstruction and the potential costs of AI [$], Proceedings of the National Academy of Sciences (DOI: 10.1073/pnas.1907377117)


Original Submission

 
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  • (Score: 0) by Anonymous Coward on Wednesday May 13 2020, @07:32PM

    by Anonymous Coward on Wednesday May 13 2020, @07:32PM (#993903)

    Your failure at reading comprehension is noted as well.
    In the interest of science, which part(s) of "AI could take a low-resolution image and make it into a high-resolution version. AI algorithms 'learn' to reconstruct images based on training from previous data" are too hard for you to understand?