Finding abnormal lymph nodes in radiological images is highly important for various medical tasks such as cancer metastasis staging and radiotherapy planning. Lymph nodes (LNs) are small glands scattered throughout the body. They are grouped or defined to various LN stations according to their anatomical locations. The CT imaging appearance and context of LNs in different stations vary significantly, posing challenges for automated detection, especially for pathological LNs. Motivated by this observation, we propose a novel end-to-end framework to improve LN detection performance by leveraging their station information. We design a multi-head detector and make each head focus on differentiating the LN and non-LN structures of certain stations. Pseudo station labels are generated by an LN station classifier as a form of multi-task learning during training, so we do not need another explicit LN station prediction model during inference. Our algorithm is evaluated on 82 patients with lung cancer and 91 patients with esophageal cancer. The proposed implicit station stratification method improves the detection sensitivity of thoracic lymph nodes from 65.1% to 71.4% and from 80.3% to 85.5% at 2 false positives per patient on the two datasets, respectively, which significantly outperforms various existing state-of-the-art baseline techniques such as nnUNet, nnDetection and LENS.

翻译：在放射影像中检测异常淋巴结对于肿瘤转移分期及放疗计划等多种医疗任务至关重要。淋巴结是散布于全身的小型腺体，根据解剖位置被归类为不同站点的淋巴结。不同站点的淋巴结在CT影像中的外观和上下文信息差异显著，这给自动化检测（尤其是病理性淋巴结检测）带来了挑战。基于这一发现，我们提出了一种全新的端到端框架，通过利用淋巴结的站信息来提升检测性能。我们设计了一个多头检测器，使每个检测头专注于区分特定站点的淋巴结与非淋巴结结构。通过多任务学习机制，在训练过程中利用淋巴结站分类器生成伪站标签，从而在推理阶段无需显式的站预测模型。该算法在82例肺癌患者和91例食管癌患者的评估数据上进行了测试。所提出的隐式站分层方法在两个数据集上（每例患者2个假阳性条件下）分别将胸部淋巴结检测灵敏度从65.1%提升至71.4%，从80.3%提升至85.5%，显著优于nnUNet、nnDetection和LENS等多种现有先进基线技术。