Cross Attention Densely Connected Networks for Multiple Sclerosis Lesion Segmentation Automatic lesion segmentation on conventional magnetic resonance imaging is an essential component in disease diagnosis, assessment, and followup. Recently, extensive deep neural networks have been designed for automatic lesion segmentation. However, these approaches are not easy to further optimize owing to the poor interpretability. In this paper, we present a novel cross attention denselyconnected network(CADCN) for multiple sclerosis lesion segmentation, which integrates attention mechanism into the encoderdecoder architecture. Aiming for further improving the performance of the model, we propose a comprehensive cross attention mechanism module by combining the characteristics of spatial and channel domains. Our method is evaluated on the public International Symposium on Biomedical Imaging (ISBI) 2015 multiple sclerosis segmentation challenge. At the time of submission, our method was amongst the top performing solution.

Dense EncoderDecoder Network based on TwoLevel Context Enhanced Residual Attention Mechanism for Segmentation of Breast Tumors in Magnetic Resonance Imaging Aiming to effective early detection of breast cancer, automatic tumor segmentation based on breast Magnetic Resonance Imaging (MRI) is concentrated by more and more researchers. This paper proposes a dense encoderdecoder network based on twolevel context enhanced residual attention mechanism (TLCRAMDED). With respect to TLCRAMDED, we design the encoding structure combining twolevel residual attention structure with dense block to extract and refine the features of different layers. Meanwhile, a dense multiscale atrous convolution is used at the end of the encoder to obtain a larger receptive field and enrich the extracted semantic information. Moreover, residual attention structure (RAS) is also used for the refinement during decoding stage, while a long connection formed with the encoder RAS output is applied to supplement the features and to gradually recover the segmentation details. We validated prosed model in the DCE sequence of challenging breast cancer MRI dataset. The average Dice coefficient is up to 81.04%, which outperforms compared stateofthearts.