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In this paper, we present a novel unsupervised domain adaptation (UDA) method, named Domain Adaptive Relational Reasoning (DARR), to generalize 3D multi-organ segmentation models to medical data collected from different scanners and/or protocols (domains).
In both datasets, results indicate that our method can achieve the highest level of accuracy while requiring a comparable or lower time complexity.
To evaluate the applicability of the ITL approach, we adopted the brain tissue annotation label on the source domain dataset of Magnetic Resonance Imaging (MRI) images to the task of brain tumor segmentation on the target domain dataset of MRI.
We prove the existence of such a clone given that infinite number of data points can be sampled from the source distribution.
In many Machine Learning domains, datasets are characterized by highly imbalanced and overlapping classes.
More specifically, we first propose a nuclei inpainting mechanism to remove the auxiliary generated objects in the synthesized images.
The performance of machine learning algorithms is known to be negatively affected by possible mismatches between training (source) and test (target) data distributions.
We introduce a novel UDA framework where a standard supervised loss on labeled synthetic data is supported by an adversarial module and a self-training strategy aiming at aligning the two domain distributions.
Unsupervised domain adaptation has attracted growing research attention on semantic segmentation.