MVP-Net: Multi-view FPN with Position-aware Attention for Deep Universal Lesion Detection
This work addresses the data-hunger problem in medical imaging for radiologists by making lesion detection more efficient with limited annotations.
The paper tackled universal lesion detection in CT images by incorporating clinical domain knowledge into a multi-view FPN with position-aware attention, achieving a 5.65% absolute gain in sensitivity over the previous state-of-the-art on the NIH DeepLesion dataset.
Universal lesion detection (ULD) on computed tomography (CT) images is an important but underdeveloped problem. Recently, deep learning-based approaches have been proposed for ULD, aiming to learn representative features from annotated CT data. However, the hunger for data of deep learning models and the scarcity of medical annotation hinders these approaches to advance further. In this paper, we propose to incorporate domain knowledge in clinical practice into the model design of universal lesion detectors. Specifically, as radiologists tend to inspect multiple windows for an accurate diagnosis, we explicitly model this process and propose a multi-view feature pyramid network (FPN), where multi-view features are extracted from images rendered with varied window widths and window levels; to effectively combine this multi-view information, we further propose a position-aware attention module. With the proposed model design, the data-hunger problem is relieved as the learning task is made easier with the correctly induced clinical practice prior. We show promising results with the proposed model, achieving an absolute gain of $\mathbf{5.65\%}$ (in the sensitivity of FPs@4.0) over the previous state-of-the-art on the NIH DeepLesion dataset.