SDF-Net：一种用于增强CT图像纵隔淋巴结检测的混合检测网络
SDF-Net: A Hybrid Detection Network for Mediastinal Lymph Node Detection on Contrast CT Images
AI总结：
本文提出融合分割与检测特征的 SDF-Net，并通过自动融合模块和形状自适应高斯核，在无掩码标注条件下提升增强CT纵隔淋巴结检测性能。
摘要：
在增强CT图像上，准确的淋巴结检测和量化对于癌症诊断和分期至关重要，因为它们会影响治疗规划和预后。然而，由于纵隔区域淋巴结对比度低、形状不规则且分布分散，检测该区域的淋巴结面临挑战。本文提出一种 Swin-Det Fusion Network（SDF-Net）以有效检测淋巴结。SDF-Net 融合来自分割和检测的特征，以增强对各种形状和大小淋巴结的检测能力。具体而言，设计了一个自动融合模块，用于在不同层级合并分割网络与检测网络的特征图。为了在没有掩码标注的情况下促进有效学习，本文引入形状自适应高斯核，在训练阶段表示淋巴结，并提供更多解剖信息以实现有效学习。对比结果表明，该方法在解决复杂的淋巴结检测问题方面具有良好性能。
英文摘要：
Accurate lymph node detection and quantification are crucial for cancer diagnosis and staging on contrast-enhanced CT images, as they impact treatment planning and prognosis. However, detecting lymph nodes in the mediastinal area poses challenges due to their low contrast, irregular shapes and dispersed distribution. In this paper, we propose a Swin-Det Fusion Network (SDF-Net) to effectively detect lymph nodes. SDF-Net integrates features from both segmentation and detection to enhance the detection capability of lymph nodes with various shapes and sizes. Specifically, an auto-fusion module is designed to merge the feature maps of segmentation and detection networks at different levels. To facilitate effective learning without mask annotations, we introduce a shape-adaptive Gaussian kernel to represent lymph node in the training stage and provide more anatomical information for effective learning. Comparative results demonstrate promising performance in addressing the complex lymph node detection problem.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
