Unsupervised domain adaptation (UDA) has been a vital protocol for migrating
information learned from a labeled source domain to facilitate the
implementation in an unlabeled heterogeneous target domain. Although UDA is
typically jointly trained on data from both domains, accessing the labeled
source domain data is often restricted, due to concerns over patient data
privacy or intellectual property. To sidestep this, we propose "off-the-shelf
(OS)" UDA (OSUDA), aimed at image segmentation, by adapting an OS segmentor
trained in a source domain to a target domain, in the absence of source domain
data in adaptation. Toward this goal, we aim to develop a novel batch-wise
normalization (BN) statistics adaptation framework. In particular, we gradually
adapt the domain-specific low-order BN statistics, e.g., mean and variance,
through an exponential momentum decay strategy, while explicitly enforcing the
consistency of the domain shareable high-order BN statistics, e.g., scaling and
shifting factors, via our optimization objective. We also adaptively quantify
the channel-wise transferability to gauge the importance of each channel, via
both low-order statistics divergence and a scaling factor.~Furthermore, we
incorporate unsupervised self-entropy minimization into our framework to boost
performance alongside a novel queued, memory-consistent self-training strategy
to utilize the reliable pseudo label for stable and efficient unsupervised
adaptation. We evaluated our OSUDA-based framework on both cross-modality and
cross-subtype brain tumor segmentation and cardiac MR to CT segmentation tasks.
Our experimental results showed that our memory consistent OSUDA performs
better than existing source-relaxed UDA methods and yields similar performance
to UDA methods with source data.

本文提出了一种新颖的批量规范化（BN）统计适应框架，其中逐渐调整域特定的低阶 BN 统计量（例如平均值和方差），同时通过优化目标明确强制执行域共享的高阶 BN 统计量的一致性（例如缩放和移位因子）进行的非监督领域自适应，该框架与基于假标签的自训练策略相结合，实现了在没有源域数据的情况下的图像分割任务的迁移。

面向源领域放宽的医学图像分割的内存一致的无监督即插即用模型适应

Memory Consistent Unsupervised Off-the-Shelf Model Adaptation for Source-Relaxed Medical Image Segmentation

Graph neural networks (GNNs) have been widely applied to numerous fields. A
recent work which combines layered structure and residual connection proposes
an improved deep architecture to extend CAmouflage-REsistant GNN (CARE-GNN) to
deep models named as Residual Layered CARE-GNN (RLC-GNN), which forms
self-correcting and incremental learning mechanism, and achieves significant
performance improvements on fraud detection task. However, we spot three issues
of RLC-GNN, which are the usage of neighboring information reaching limitation,
the training difficulty which is inherent problem to deep models and lack of
comprehensive consideration about node features and external patterns. In this
work, we propose three approaches to solve those three problems respectively.
First, we suggest conducting similarity measure via cosine distance to take
both local features and external patterns into consideration. Then, we combine
the similarity measure module and the idea of adjacency-wise normalization with
node-wise and batch-wise normalization and then propound partial neighborhood
normalization methods to overcome the training difficulty while mitigating the
impact of too much noise caused by high-density of graph. Finally, we put
forward intermediate information supplement to solve the information
limitation. Experiments are conducted on Yelp and Amazon datasets. And the
results show that our proposed methods effectively solve the three problems.
After applying the three methods, we achieve 4.81%, 6.62% and 6.81%
improvements in the metrics of recall, AUC and Macro-F1 respectively on the
Yelp dataset. And we obtain 1.65% and 0.29% improvements in recall and AUC
respectively on the Amazon datasets.

本文提出三个方法以解决 Residual Layered CARE-GNN 的三个问题：采用余弦距离进行相似度度量，结合邻节点和批次规范化的相似度量模块，以及中间信息补充。在 Yelp 和 Amazon 数据集上，采用这三种方法分别获得了 4.81％，6.62％，6.81％的提高。