A volumetric attention(VA) module for 3D medical image segmentation and detection is proposed. VA attention is inspired by recent advances in video processing, enables 2.5D networks to leverage context information along the z direction, and allows the use of pretrained 2D detection models when training data is limited, as is often the case for medical applications. Its integration in the Mask R-CNN is shown to enable state-of-the-art performance on the Liver Tumor Segmentation (LiTS) Challenge, outperforming the previous challenge winner by 3.9 points and achieving top performance on the LiTS leader board at the time of paper submission. Detection experiments on the DeepLesion dataset also show that the addition of VA to existing object detectors enables a 69.1 sensitivity at 0.5 false positive per image, outperforming the best published results by 6.6 points.
@article{arxiv.2004.01997,
title = {Volumetric Attention for 3D Medical Image Segmentation and Detection},
author = {Xudong Wang and Shizhong Han and Yunqiang Chen and Dashan Gao and Nuno Vasconcelos},
journal= {arXiv preprint arXiv:2004.01997},
year = {2020}
}