标题： HyperSORT：具有超网络的自组织鲁棒训练 显示英文标题

标题： HyperSORT: Self-Organising Robust Training with hyper-networks

摘要： 医学影像数据集通常包含从错误标签到不一致的标签风格的各种偏差。 这些偏差可能对深度分割网络的性能产生负面影响。 然而，识别和描述这些偏差是一项特别繁琐且具有挑战性的任务。 在本文中，我们引入了HyperSORT，一种框架，该框架使用超网络从表示图像和注释变异性的潜在向量中预测UNet的参数。 超网络的参数和对应于训练集中每个数据样本的潜在向量集合是共同学习的。 因此，与其优化一个神经网络以适应数据集， HyperSORT学习了一个复杂的UNet参数分布，其中低密度区域可以捕捉特定噪声模式，而较大的模式则以区分但有意义的方式稳健地分割器官。 我们在两个3D腹部CT公开数据集上验证了我们的方法：首先是AMOS数据集的合成扰动版本，以及TotalSegmentator，这是一个包含真实未知偏差和错误的大规模数据集。 我们的实验表明，HyperSORT创建了数据集的结构化映射，使得能够识别相关的系统性偏差和错误样本。 潜在空间聚类生成的UNet参数按照底层学习到的系统性偏差执行分割任务。 代码和我们对TotalSegmentator数据集的分析已公开：https://github.com/ImFusionGmbH/HyperSORT 显示英文摘要

摘要： Medical imaging datasets often contain heterogeneous biases ranging from erroneous labels to inconsistent labeling styles. Such biases can negatively impact deep segmentation networks performance. Yet, the identification and characterization of such biases is a particularly tedious and challenging task. In this paper, we introduce HyperSORT, a framework using a hyper-network predicting UNets' parameters from latent vectors representing both the image and annotation variability. The hyper-network parameters and the latent vector collection corresponding to each data sample from the training set are jointly learned. Hence, instead of optimizing a single neural network to fit a dataset, HyperSORT learns a complex distribution of UNet parameters where low density areas can capture noise-specific patterns while larger modes robustly segment organs in differentiated but meaningful manners. We validate our method on two 3D abdominal CT public datasets: first a synthetically perturbed version of the AMOS dataset, and TotalSegmentator, a large scale dataset containing real unknown biases and errors. Our experiments show that HyperSORT creates a structured mapping of the dataset allowing the identification of relevant systematic biases and erroneous samples. Latent space clusters yield UNet parameters performing the segmentation task in accordance with the underlying learned systematic bias. The code and our analysis of the TotalSegmentator dataset are made available: https://github.com/ImFusionGmbH/HyperSORT