用可变形检测变压器解决肺结节稀疏检测难题
Lung-DETR: Deformable Detection Transformer for Sparse Lung Nodule Anomaly Detection
- 将肺结节检测重构为异常检测任务，利用7.5mm最大密度投影融合切片
- 在LUNA16数据集上达到94.2%的F1分数（召回率95.2%，精确率93.3%）
- 适合临床真实场景下稀疏肺结节检测，尤其适用于低阳性率数据
由于肺结节在真实临床数据中出现稀疏且与其它解剖结构相似，计算机断层扫描（CT）图像中的准确肺结节检测极具挑战性。典型阳性病例中，结节可能仅出现在3%的CT切片中，加剧了检测难度。为此，本文将问题重构为异常检测任务，聚焦于以正常数据为主的数据集中罕见结节的识别。提出一种新方法，结合自定义数据预处理与可变形检测变压器（Deformable-DETR）。采用7.5mm最大密度投影（MIP）将相邻肺部切片合并为单张图像，减少切片数量并降低结节稀疏度，增强空间上下文信息，从而更好区分结节与复杂血管结构及支气管等。使用自定义焦点损失函数处理数据不平衡问题。模型在反映真实临床数据分布的稀疏肺结节数据集上取得当前最优性能，在LUNA16数据集上达到94.2%的F1分数（召回率95.2%，精确率93.3%）。
原文摘要 · Abstract (English)
Accurate lung nodule detection for computed tomography (CT) scan imagery is challenging in real-world settings due to the sparse occurrence of nodules and similarity to other anatomical structures. In a typical positive case, nodules may appear in as few as 3% of CT slices, complicating detection. To address this, we reframe the problem as an anomaly detection task, targeting rare nodule occurrences in a predominantly normal dataset. We introduce a novel solution leveraging custom data preprocessing and Deformable Detection Transformer (Deformable- DETR). A 7.5mm Maximum Intensity Projection (MIP) is utilized to combine adjacent lung slices into single images, reducing the slice count and decreasing nodule sparsity. This enhances spatial context, allowing for better differentiation between nodules and other structures such as complex vascular structures and bronchioles. Deformable-DETR is employed to detect nodules, with a custom focal loss function to better handle the imbalanced dataset. Our model achieves state-of-the-art performance on the LUNA16 dataset with an F1 score of 94.2% (95.2% recall, 93.3% precision) on a dataset sparsely populated with lung nodules that is reflective of real-world clinical data.
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