Automatic correction of performance drift under acquisition shift in medical image classification

Mélanie Roschewitz (), Galvin Khara, Joe Yearsley, Nisha Sharma, Jonathan J. James, Éva Ambrózay, Adam Heroux, Peter Kecskemethy, Tobias Rijken and Ben Glocker ()
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Mélanie Roschewitz: Kheiron Medical Technologies
Galvin Khara: Kheiron Medical Technologies
Joe Yearsley: Kheiron Medical Technologies
Nisha Sharma: Leeds Teaching Hospital NHS Trust, Department of Radiology
Jonathan J. James: Nottingham University Hospitals NHS Trust, Nottingham City Hospital, Nottingham Breast Institute
Éva Ambrózay: MaMMa Egészségügyi Zrt.
Adam Heroux: Kheiron Medical Technologies
Peter Kecskemethy: Kheiron Medical Technologies
Tobias Rijken: Kheiron Medical Technologies
Ben Glocker: Kheiron Medical Technologies

Nature Communications, 2023, vol. 14, issue 1, 1-10

Abstract: Abstract Image-based prediction models for disease detection are sensitive to changes in data acquisition such as the replacement of scanner hardware or updates to the image processing software. The resulting differences in image characteristics may lead to drifts in clinically relevant performance metrics which could cause harm in clinical decision making, even for models that generalise in terms of area under the receiver-operating characteristic curve. We propose Unsupervised Prediction Alignment, a generic automatic recalibration method that requires no ground truth annotations and only limited amounts of unlabelled example images from the shifted data distribution. We illustrate the effectiveness of the proposed method to detect and correct performance drift in mammography-based breast cancer screening and on publicly available histopathology data. We show that the proposed method can preserve the expected performance in terms of sensitivity/specificity under various realistic scenarios of image acquisition shift, thus offering an important safeguard for clinical deployment.

Date: 2023
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DOI: 10.1038/s41467-023-42396-y

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