Nasopharyngeal carcinoma (NPC) is a prevalent and clinically significant
malignancy that predominantly impacts the head and neck area. Precise
delineation of the Gross Tumor Volume (GTV) plays a pivotal role in ensuring
effective radiotherapy for NPC. Despite recent methods that have achieved
promising results on GTV segmentation, they are still limited by lacking
carefully-annotated data and hard-to-access data from multiple hospitals in
clinical practice. Although some unsupervised domain adaptation (UDA) has been
proposed to alleviate this problem, unconditionally mapping the distribution
distorts the underlying structural information, leading to inferior
performance. To address this challenge, we devise a novel Sourece-Free Active
Domain Adaptation (SFADA) framework to facilitate domain adaptation for the GTV
segmentation task. Specifically, we design a dual reference strategy to select
domain-invariant and domain-specific representative samples from a specific
target domain for annotation and model fine-tuning without relying on
source-domain data. Our approach not only ensures data privacy but also reduces
the workload for oncologists as it just requires annotating a few
representative samples from the target domain and does not need to access the
source data. We collect a large-scale clinical dataset comprising 1057 NPC
patients from five hospitals to validate our approach. Experimental results
show that our method outperforms the UDA methods and achieves comparable
results to the fully supervised upper bound, even with few annotations,
highlighting the significant medical utility of our approach. In addition,
there is no public dataset about multi-center NPC segmentation, we will release
code and dataset for future research.

提出了一种新颖的无源主动域适应（SFADA）框架，用于优化鼻咽癌（NPC）中的肿瘤体积分割任务，通过选择特定目标域中的领域不变和领域特定样本进行标注和模型微调，既确保了数据隐私，又减轻了肿瘤学家的工作量。

多医院鼻咽癌肿瘤分割的双参照源无需活动领域适应

Dual-Reference Source-Free Active Domain Adaptation for Nasopharyngeal  Carcinoma Tumor Segmentation across Multiple Hospitals

Domain adaptation (DA) has been widely applied in the diabetic retinopathy
(DR) grading of unannotated ultra-wide-field (UWF) fundus images, which can
transfer annotated knowledge from labeled color fundus images. However,
suffering from huge domain gaps and complex real-world scenarios, the DR
grading performance of most mainstream DA is far from that of clinical
diagnosis. To tackle this, we propose a novel source-free active domain
adaptation (SFADA) in this paper. Specifically, we focus on DR grading problem
itself and propose to generate features of color fundus images with
continuously evolving relationships of DRs, actively select a few valuable UWF
fundus images for labeling with local representation matching, and adapt model
on UWF fundus images with DR lesion prototypes. Notably, the SFADA also takes
data privacy and computational efficiency into consideration. Extensive
experimental results demonstrate that our proposed SFADA achieves
state-of-the-art DR grading performance, increasing accuracy by 20.9% and
quadratic weighted kappa by 18.63% compared with baseline and reaching 85.36%
and 92.38% respectively. These investigations show that the potential of our
approach for real clinical practice is promising.

本研究提出了一种新颖的无源主动域适应方法 (SFADA)，通过生成具有不断演化的 DR 关系特征的彩色眼底图像，通过局部表示匹配主动选择一些有价值的 UWF 眼底图像进行标注，并利用 DR 病变原型在 UWF 眼底图像上进行模型适应。实验结果表明，与基准方法相比，我们提出的 SFADA 在 DR 分级性能上达到了最先进的水平，准确率提高了 20.9%，加权 kappa 系数提高了 18.63%，分别达到了 85.36% 和 92.38%。这些研究结果显示了我们方法在实际临床实践中的潜力。