论文标题
通过有监督的预培训来重新访问3D上下文建模，以用于CT切片中的通用病变检测
Revisiting 3D Context Modeling with Supervised Pre-training for Universal Lesion Detection in CT Slices
论文作者
论文摘要
来自计算机断层扫描（CT）切片的通用病变检测对于综合疾病筛查很重要。由于每个病变都可以位于多个相邻切片中，因此3D上下文建模对于开发自动病变检测算法具有重要意义。在这项工作中，我们提出了一个修改后的伪-3D特征金字塔网络（MP3D FPN），该网络利用了深度可分离的卷积过滤器和组变换模块（GTM），以有效提取CT Slices中通用病态检测的3D上下文增强2D的增强2D。为了促进更快的收敛速度，使用自然图像域中的仅大型2D对象检测数据集得出了一种新颖的3D网络预训练方法。我们证明，通过新颖的预训练方法，提出的MP3D FPN在深层数据集上实现了最先进的检测性能（[email protected]的敏感性的绝对提高了3.48％），可显着超过基线方法，最高可容纳6.06％的基线方法（在MAP@0 @0@@0 @0 @0 @0）中采用3D差异模型。此外，拟议的3D预训练权重可以用于提高其他3D医学图像分析任务的性能。
Universal lesion detection from computed tomography (CT) slices is important for comprehensive disease screening. Since each lesion can locate in multiple adjacent slices, 3D context modeling is of great significance for developing automated lesion detection algorithms. In this work, we propose a Modified Pseudo-3D Feature Pyramid Network (MP3D FPN) that leverages depthwise separable convolutional filters and a group transform module (GTM) to efficiently extract 3D context enhanced 2D features for universal lesion detection in CT slices. To facilitate faster convergence, a novel 3D network pre-training method is derived using solely large-scale 2D object detection dataset in the natural image domain. We demonstrate that with the novel pre-training method, the proposed MP3D FPN achieves state-of-the-art detection performance on the DeepLesion dataset (3.48% absolute improvement in the sensitivity of [email protected]), significantly surpassing the baseline method by up to 6.06% (in [email protected]) which adopts 2D convolution for 3D context modeling. Moreover, the proposed 3D pre-trained weights can potentially be used to boost the performance of other 3D medical image analysis tasks.
