Dong Ni

TL;DR: In this article, a new deep attentive modules with mixed kernel convolutions within a fully convolutional neural network architecture that utilizes deep supervision and residual connections was proposed for cortical plate segmentation.

TL;DR: Wang et al. as discussed by the authors proposed a boundary-rendering framework that explicitly highlights the importance of boundary for automated nodule segmentation in Breast Ultrasound images, which utilizes a boundary selection module to automatically focus on the ambiguous boundary region and a graph convolutional-based boundary rendering module to exploit global contour information.

TL;DR: In this paper , a coarse-to-fine framework is proposed to boost the small myocardial pathology region segmentation with shape prior knowledge, where the coarse segmentation model identifies the left ventricle myocardia structure as a shape prior, and the fine segmentation models integrates a pixel-wise attention strategy with an auto-weighted supervision model to learn and extract salient pathological structures from the multi-sequence CMR data.

TL;DR: The promising potential of radiomic analysis of CT images for differentiating between CIP and RP in lung cancer could be a useful tool to attribute the cause of pneumonitis in patients who receive both ICI and RT.