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arXiv · 2501.09101

Relation U-Net

Sheng He·Rina Bao·P. Ellen Grant·Yangming Ou
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Abstract

Towards clinical interpretations, this paper presents a new ''output-with-confidence'' segmentation neural network with multiple input images and multiple output segmentation maps and their pairwise relations. A confidence score of the test image without ground-truth can be estimated from the difference among the estimated relation maps. We evaluate the method based on the widely used vanilla U-Net for segmentation and our new model is named Relation U-Net which can output segmentation maps of the input images as well as an estimated confidence score of the test image without ground-truth. Experimental results on four public datasets show that Relation U-Net can not only provide better accuracy than vanilla U-Net but also estimate a confidence score which is linearly correlated to the segmentation accuracy on test images.

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Sheng He, Rina Bao, P. Ellen Grant, Yangming Ou. 2025-01-15. Relation U-Net. https://arxiv.org/abs/2501.09101

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