Abstract：
Medical image segmentation has made remarkable progress with advances in deep learning technology, depending on the quality and quantity of labeled data. Although various deep learning model structures and training methods have been proposed and high performance has been published, limitations such as inter-class accuracy bias exist in actual clinical applications, especially due to the significant lack of small object performance in multi-organ segmentation tasks. In this paper, we propose an uncertainty-based contrastive learning technique, namely UncerNCE, with an optimal hybrid architecture for high classification and segmentation performance of small organs. Our backbone architecture adopts a hybrid network that employs both convolutional and transformer layers, which have demonstrated remarkable performance in recent years. The key proposal of this study addresses the multi-class accuracy bias and resolves a common tradeoff in existing studies between segmenting regions of small objects and reducing overall noise (i.e., false positives). Uncertainty based contrastive learning based on the proposed hybrid network performs spotlight learning on selected regions based on uncertainty and achieved accurate segmentation for all classes while suppressing noise. Comparison with state-of-the-art techniques demonstrates the superiority of our results on BTCV and 1K data.
摘要：
随着深度学习技术的进步，医学图像分割取得了显著进展，取决于标记数据的质量和数量。尽管已经提出了各种深度学习模型结构和训练方法，并且已经发布了高性能，在实际临床应用中存在类间准确性偏差等限制，特别是由于在多器官分割任务中严重缺乏小对象性能。在本文中,我们提出了一种基于不确定性的对比学习技术，即Uncernce,具有最佳的混合架构，可实现小器官的高分类和分割性能。我们的骨干架构采用混合网络，同时采用卷积层和变压器层，近年来表现显著。本研究的主要建议解决了多类精度偏差，并解决了现有研究中分割小物体区域和减少整体噪声之间的常见权衡（即，假阳性)。基于所提出的混合网络的基于不确定性的对比学习对基于不确定性的选定区域进行聚光灯学习，并在抑制噪声的同时实现对所有类别的准确分割。与最新技术的比较证明了我们的结果在BTCV和1K数据上的优越性。
