Weakly Supervised 3D Brain Lesion Segmentation

In this work, we propose a new weakly supervised 3D brain lesion segmentation approach using attentional representation learning. Our approach only requires image-level labels, and is able to produce accurate segmentation of the 3D lesion volumes. To achieve that, we design a novel dimensional independent attention mechanism on top of the Class Activation Maps (CAMs), which refines the 3D CAMs to obtain better estimates of the lesion volumes, without introducing significantly more trainable variables. The generated attentional CAMs are then used as a source of weak supervision signals to learn a representation model, which can reliably separate the voxels belong to the lesion volumes from those of the normal tissues.

For more details please see the following paper:

Weakly Supervised Brain Lesion Segmentation via Attentional Representation Learning.
Kai Wu, Bowen Du, Man Luo, Hongkai Wen, Yiran Shen, Jianfeng Feng.
In Proceedings of the 22nd International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI), pp.211-219, 2019.