MLANet: Multi-Layer Anchor-free Network for generic lesion detection

In medical image processing, detecting lesions from computed tomography (CT) scans becomes an important research problem with increasing attention. However, this problem is nontrivial because lesions from different organs and parts reflect different characteristics as well as different sizes. Most conventional methods only use a single-scale architecture to detect lesion areas. To get rid of the drawbacks above in medical imaging, a multi-scale framework called MLANet is proposed. To deal with the scale imbalance problem, we design a new backbone & mdash;a mixed hourglass network, in which each hourglass module share different input sizes and orders to extract features from different scales. And then the information is sent to the proposed Strengthen Weighted Feature Pyramid Network (SWFPN), a multi-layer weighted feature fusion module, to combine more semantic and spatial information, especially for the case where the number of layers is small. Finally, a Center-to-Corner (C2C) transformation is proposed to deal with the inaccurate size prediction of lesions. It is a non-linear transformation function, aiming to make the predictions more stable and accurate. MLANet is an end-to-end network and is easy to train. In our experiment, it achieves 65.2% AP50, as well as 88.3% in the sensitivity of FPs@4.0 on the DeepLesion dataset, which exceeds many state-of-the-art detectors.