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Linear optimal transport subspaces for point set classification
Our approach employs the Linear Optimal Transport (LOT) transform to obtain a linear embedding of set-structured data.
A sliced-Wasserstein distance-based approach for out-of-class-distribution detection
These methods often require extensive training data, are computationally expensive, and are vulnerable to out-of-distribution samples, e. g., adversarial attacks.
Transport-based morphometry of nuclear structures of digital pathology images in cancers
We demonstrate the model is robust to different staining patterns and imaging protocols, and can be used to discover meaningful and interpretable information within and across datasets and cancer types.
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