计算机断层扫描（CT）扫描中准确、自动检测盆腔淋巴结对于诊断结直肠癌淋巴结转移至关重要，而这对于结直肠癌的分期、治疗计划、手术指导和术后随访起着至关重要的作用。癌症。然而，由于这些节点较小且尺寸可变，以及复杂的盆腔 CT 图像中存在大量相似信号，实现高检测灵敏度和特异性提出了挑战。为了解决这些问题，我们提出了一个 3D 特征感知在线调整网络（FAOT-Net），它引入了一种新颖的 1.5 阶段结构，通过我们的在线候选调整过程无缝集成检测和细化，并通过定制的功能流程。此外，我们重新设计了锚点拟合和锚点匹配策略，以近乎无超参数的方式进一步提高检测性能。我们的框架在 PLN 数据集上的每次扫描出现 16 个误报，FROC 得分为 52.8，灵敏度为 91.7%。代码可在以下网址获取：github.com/SCUsomebody/FAOT-Net/。

Accurate and automatic detection of pelvic lymph nodes in computed tomography (CT) scans is critical for diagnosing lymph node metastasis in colorectal cancer, which in turn plays a crucial role in its staging, treatment planning, surgical guidance, and postoperative follow-up of colorectal cancer. However, achieving high detection sensitivity and specificity poses a challenge due to the small and variable sizes of these nodes, as well as the presence of numerous similar signals within the complex pelvic CT image. To tackle these issues, we propose a 3D feature-aware online-tuning network (FAOT-Net) that introduces a novel 1.5-stage structure to seamlessly integrate detection and refinement via our online candidate tuning process and takes advantage of multi-level information through the tailored feature flow. Furthermore, we redesign the anchor fitting and anchor matching strategies to further improve detection performance in a nearly hyperparameter-free manner. Our framework achieves the FROC score of 52.8 and the sensitivity of 91.7% with 16 false positives per scan on the PLNDataset. Code will be available at: github.com/SCUsomebody/FAOT-Net/.