This paper presents a Lead-Assisting Backbone Attention Network (LABANet), which is able to perform multi-pathology instance segmentation of dental panoramic X-rays. A Lead-Assisting Attention Backbone (LAAB), containing two Swin-Transformers, is first developed for feature extraction. The following Region Proposal Network (RPN) and RoIAlign modules further convert the extracted features to a fixed-size feature map. Finally, an improved attention head with a Squeeze-and-Excitation (SE) block is constructed for object classification, bounding-box regression, and mask segmentation. By taking advantage of the global attention mechanism, the LABANet can better achieve multiple pathology segmentation. Experiment results demonstrate its effectiveness and advantages over state-of-the-art methods.