Abstract
Consideration of subgroups or domains within medical image datasets is crucial for the development and evaluation of robust and generalizable machine learning systems. To tackle the domain identification problem, we examine deep unsupervised generative clustering approaches for representation learning and clustering. The Variational Deep Embedding (VaDE) model is trained to learn lower-dimensional representations of images based on a Mixture-of-Gaussians latent space prior distribution while optimizing cluster assignments. We propose the Conditionally Decoded Variational Deep Embedding (CDVaDE) model which incorporates additional variables of choice, such as the class labels, as conditioning factors to guide the clustering towards subgroup structures in the data which have not been known or recognized previously. We analyze the behavior of CDVaDE on multiple datasets and compare it to other deep clustering algorithms. Our experimental results demonstrate that the considered models are capable of separating digital pathology images into meaningful subgroups. We provide a general-purpose implementation of all considered deep clustering methods as part of the open source Python package DomId (https://github.com/DIDSR/DomId).
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Acknowledgments
The authors would like to thank Dr. Marios Gavrielides for providing access to the HER2 dataset and for helpful discussion. This project was supported in part by an appointment to the Research Participation Program at the U.S. Food and Drug Administration administered by the Oak Ridge Institute for Science and Education through an interagency agreement between the U.S. Department of Energy and the U.S. Food and Drug Administration. XS acknowledges support from the Hightech Agenda Bayern.
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Sidulova, M., Sun, X., Gossmann, A. (2023). Deep Unsupervised Clustering for Conditional Identification of Subgroups Within a Digital Pathology Image Set. In: Greenspan, H., et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14227. Springer, Cham. https://doi.org/10.1007/978-3-031-43993-3_64
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