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• W3097096051 abstract "The need for training data can impede the adoption of novel imaging modalities for learning-based medical image analysis. 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. Our work addresses the challenge of adapting to a more informative target domain where multiple target samples can emerge from a single source sample. In particular we consider translating from mp-MRI to VERDICT, a richer MRI modality involving an optimized acquisition protocol for cancer characterization. We explicitly account for the inherent uncertainty of this mapping and exploit it to generate multiple outputs conditioned on a single input. Our results show that this allows us to extract systematically better image representations for the target domain, when used in tandem with both simple, CycleGAN-based baselines, as well as more powerful approaches that integrate discriminative segmentation losses and/or residual adapters. When compared to its deterministic counterparts, our approach yields substantial improvements across a broad range of dataset sizes, increasingly strong baselines, and evaluation measures." @default.
• W3097096051 created "2020-11-09" @default.
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• W3097096051 date "2020-01-01" @default.
• W3097096051 modified "2023-10-16" @default.
• W3097096051 title "Harnessing Uncertainty in Domain Adaptation for MRI Prostate Lesion Segmentation" @default.
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• W3097096051 doi "https://doi.org/10.1007/978-3-030-59710-8_50" @default.
• W3097096051 hasPublicationYear "2020" @default.
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