3D Context Enhanced Region-based Convolutional Neural Network for End-to-End Lesion Detection
This work addresses the challenge of lesion detection in medical imaging, which is important for healthcare diagnostics, but it appears incremental as it builds on existing 2D CNN methods by adding 3D context.
The paper tackles the problem of detecting lesions from CT scans by proposing 3DCE, a method that incorporates 3D context into a CNN framework, resulting in effective lesion detection as demonstrated on the DeepLesion dataset.
Detecting lesions from computed tomography (CT) scans is an important but difficult problem because non-lesions and true lesions can appear similar. 3D context is known to be helpful in this differentiation task. However, existing end-to-end detection frameworks of convolutional neural networks (CNNs) are mostly designed for 2D images. In this paper, we propose 3D context enhanced region-based CNN (3DCE) to incorporate 3D context information efficiently by aggregating feature maps of 2D images. 3DCE is easy to train and end-to-end in training and inference. A universal lesion detector is developed to detect all kinds of lesions in one algorithm using the DeepLesion dataset. Experimental results on this challenging task prove the effectiveness of 3DCE. We have released the code of 3DCE in https://github.com/rsummers11/CADLab/tree/master/lesion_detector_3DCE.