Multi-task uncertainty-constrained GAN(Mt-UcGAN) for joint segmentation, quantification and uncertainty estimation of renal tumors on CT conclude that the integrated segmentation, quantification, and uncertainty estimate of renal tumors were initially proposed using a multi-task uncertainty-constrained generative adversarial network (Mt-UcGAN).
A novel adversarial technique with uncertainty limitations was put forth by us. Results from the experiment show that Mt-UcGAN can help with clinical tumor assessments.
Additionally, it can give medical professionals feedback on the validity of the findings, enabling them to undertake further visual reviews and revisions of the findings to raise the diagnostic accuracy.
The three main procedures for clinical tumor disease diagnosis are segmentation of renal tumor, measurement of tumor indices (i.e., center point coordinates, diameter, circumference, and cross-sectional area), and segmentation uncertainty estimation.
These tasks have, however, only ever been researched separately until now. It is quite difficult to describe two distinct tasks as a single optimization framework since segmentation and quantification tasks have separate optimization types.
In this study, we present a unified framework for the joint segmentation, quantification, and uncertainty estimate of renal malignancies on CT (Mt-UcGAN: Multi-task uncertainty-constrained GAN ).
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