Relation U-Net 可同时输出分割图与置信度，提升临床可解释性。
Relation U-Net
- 基于多输入图像与关系图，生成带置信度的分割结果。
- 在四个公开数据集上精度优于普通U-Net，置信度与准确率线性相关。
- 适合需要可靠性评估的医疗图像分割场景。
为支持临床解释，本文提出一种新型“带置信度输出”的分割神经网络，可处理多张输入图像和多组输出分割图及其成对关系。通过分析估计的关系图差异，可对无真实标签的测试图像估算置信度。在广泛使用的标准U-Net基础上进行评估，新模型命名为Relation U-Net，不仅能输出输入图像的分割图，还能估计测试图像的置信度。在四个公开数据集上的实验表明，Relation U-Net不仅分割精度优于标准U-Net，且其估计的置信度与测试图像的实际分割准确率呈线性相关。
原文摘要 · Abstract (English)
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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