Rui Huang

TL;DR: CA-Net as mentioned in this paper proposes a joint spatial attention module to make the network focus more on the foreground region and a novel channel attention module is proposed to adaptively recalibrate channel-wise feature responses and highlight the most relevant feature channels.

TL;DR: This work makes extensive use of multiple attentions in a CNN architecture and proposes a comprehensive attention-based CNN (CA-Net) for more accurate and explainable medical image segmentation that is aware of the most important spatial positions, channels and scales at the same time.

TL;DR: In this article, an end-to-end deep neural network was proposed to solve the problem of imbalanced large and small organ segmentation in head and neck (HaN) CT images, where a strong main network with densely connected atrous spatial pyramid pooling and squeeze-and-excitation modules was used for segmenting large organs, where large organs label maps are directly output.

TL;DR: A novel two-stage deep neural network, FocusNetv2, is proposed to solve this challenging problem by automatically locating, ROI-pooling, and segmenting small organs with specifically designed small-organ localization and segmentation sub-networks while maintaining the accuracy of large organ segmentation.

TL;DR: Zhang et al. as discussed by the authors proposed to co-train weight-averaged models for learning a unified multi-organ segmentation network from few-organ datasets, where coupled networks are trained to teach each other on un-annotated organs.