SDF-Net用自适应高斯核提升纵隔淋巴结检测，无需标注掩码。
SDF-Net: A Hybrid Detection Network for Mediastinal Lymph Node Detection on Contrast CT Images
- 融合分割与检测特征，自动融合多层特征图。
- 引入自适应高斯核表征淋巴结，提升小/不规则目标检测性能。
- 无需像素级标注，在CT图像上实现精准定位，适合医学影像分析者。
准确的淋巴结检测与量化对癌症诊断和分期至关重要，影响治疗方案制定与预后判断。然而，由于纵隔区淋巴结对比度低、形状不规则且分布分散，检测难度大。本文提出Swin-Det Fusion Network（SDF-Net），通过融合分割与检测网络的特征，增强对不同形状和大小淋巴结的检测能力。设计了自适应融合模块，在不同层级合并两类网络的特征图。为在无掩码标注条件下有效学习，提出一种形状自适应高斯核来表征淋巴结，提供更丰富的解剖信息。实验结果表明，SDF-Net在复杂淋巴结检测任务中表现优异。
原文摘要 · Abstract (English)
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.
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