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

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

SUBMITTER: Roschewitz M 

PROVIDER: S-EPMC10587231 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature