Abstract
Imaging AI models are starting to reach real clinical settings, where model drift can happen due to diverse factors. That is why model monitoring must be set up in order to prevent model degradation over time. In this context, we test and propose a data drift detection solution based on unsupervised deep learning for a breast cancer imaging setting. A convolutional autoencoder is trained on a baseline set of expected images and controlled drifts are introduced in the data in order to test if a set of metrics extracted from the reconstructions and the latent space are able to distinguish them. We prove that this is a valid tool that manages to detect subtle differences even within these complex kind of images.
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Bóbeda, J., García-González, M.J., Pérez-Herrera, L.V., López-Linares, K. (2023). Unsupervised Data Drift Detection Using Convolutional Autoencoders: A Breast Cancer Imaging Scenario. In: Chen, YW., Tanaka, S., Howlett, R.J., Jain, L.C. (eds) Innovation in Medicine and Healthcare. KES InMed 2023. Smart Innovation, Systems and Technologies, vol 357. Springer, Singapore. https://doi.org/10.1007/978-981-99-3311-2_31
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DOI: https://doi.org/10.1007/978-981-99-3311-2_31
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