March 28, 2024, 4:43 a.m. | Francesco Galati, Daniele Falcetta, Rosa Cortese, Barbara Casolla, Ferran Prados, Ninon Burgos, Maria A. Zuluaga

cs.LG updates on

arXiv:2309.06075v2 Announce Type: replace-cross
Abstract: We present a semi-supervised domain adaptation framework for brain vessel segmentation from different image modalities. Existing state-of-the-art methods focus on a single modality, despite the wide range of available cerebrovascular imaging techniques. This can lead to significant distribution shifts that negatively impact the generalization across modalities. By relying on annotated angiographies and a limited number of annotated venographies, our framework accomplishes image-to-image translation and semantic segmentation, leveraging a disentangled and semantically rich latent space to …

abstract art arxiv brain cs.lg distribution domain domain adaptation eess.iv focus framework image imaging segmentation semi-supervised state training translation type via

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