Masato Tsutsumi

The paper introduces mindGAP, a framework to quantify individual-population gaps in depressive symptom dynamics using longitudinal data, finding divergence in symptom organization associated with depression severity and traits.

This paper introduces Morpho-VAE, a deep learning framework for analyzing shell morphology, which outperforms traditional methods in species classification and reveals morphological patterns in Anadara shells.

This paper introduces Kanade, a variational autoencoder-based method that disentangles batch effects from biological signals in single-cell transcriptomics, improving comparative analysis across conditions.

The paper introduces Morpho-VAE, a landmark-free method for shape analysis using variational autoencoders, applied to primate mandible images to extract morphological features for classification.