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Found 5 papers, 4 papers with code
ssVERDICT: Self-Supervised VERDICT-MRI for Enhanced Prostate Tumour Characterisation
Results: In simulations, ssVERDICT outperforms the baseline methods (NLLS and supervised DL) in estimating all the parameters from the VERDICT prostate model in terms of Pearson's correlation coefficient, bias, and MSE.
Beyond Deterministic Translation for Unsupervised Domain Adaptation
In this work we challenge the common approach of using a one-to-one mapping ('translation') between the source and target domains in unsupervised domain adaptation (UDA).
Learning to Downsample for Segmentation of Ultra-High Resolution Images
Many computer vision systems require low-cost segmentation algorithms based on deep learning, either because of the enormous size of input images or limited computational budget.
Unsupervised Domain Adaptation with Semantic Consistency across Heterogeneous Modalities for MRI Prostate Lesion Segmentation
Firstly, we introduce a semantic cycle-consistency loss in the source domain to ensure that the translation preserves the semantics.
Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation
Domain adaptation methods partially mitigate this problem by translating training data from a related source domain to a novel target domain, but typically assume that a one-to-one translation is possible.
