To exploit multi-task consistency in denois- ing, we further introduce a Multi-Task Conditioning strategy, which can implicitly utilize the complementary nature of the tasks to help learn the unlabeled tasks, leading to an improvement in the denoising performance of the different tasks. ...
Now given a point k, which may be labeled or unlabeled, we interpret the point as a sample from the t step Markov random walk. Since labels are associated with the original (starting) points, the posterior probability of the label for point k is given by P post (y|k) = i P(y|i...
Substantial efforts are made to generate segmentation masks to characterize a given organ. The community ends up with multiple label maps of individual structures in different cases, not suitable for current multi-organ segmentation frameworks. Our objective is to leverage segmentations from multiple ...
As a label-free method, WPT is nondestructive and is not limited by the photobleaching and phototoxicity commonly associated with fluorescence microscopy. In a larger context, WPT highlights the advantage of partially coherent illumination, phase shifting, and phase-contrast geometry. The white-light ...
We show that the performance of this model is comparable to the state-of-the-art multiple instance multiple label classiffiers and that unlike some state-of-the-art models, it is scalable and practical for datasets with a large number of training instances and possible labels.;Finally we ...
pseudo-contours. The average time for pseudo-contour generation was 8 s per contour on GPU. A multi-class mask was created for each CT image, ensembling the available ground truth segmentations with the generated pseudo-contours replacing the missing labels, yielding labelmaps with eleven classes...