SynergyNet: bridging the gap between discrete and continuous representations for precise medical image segmentation.

Gorade, Vandan, et al.

WACV 20242024

SynergyNet Bridging the Gap Between Discrete and Continuous Representations for Preci 2deaf05a4c

Abstract

An abstract is not available in the lab archive. View the paper at its source.

Cite this work

Gorade, Vandan, et al. (2024). SynergyNet: bridging the gap between discrete and continuous representations for precise medical image segmentation. WACV 2024

BibTeX

Abstract

Cite this work

Related publications

IPMN risk assessment with subregional radiomics and deep learning

Multimodal AI for early prediction of adverse clinical outcomes in acute pancreatitis

Multi-center radiomics-deep lEarning fusion model for stratifying IPMN malignancy risk on MRI

Upstream probabilistic meta-imputation for multimodal pediatric pancreatitis classification

In-depth radiological and explainability analysis of deep learning predictions for IPMN risk stratification on MRI