Lan Huang ¹ Yangguang Shao ¹ Hui Yang ^2* Chunjie Guo ^3* Yan Wang ¹ Ziqi Zhao ¹ Yingchun Gong ¹

• ¹ College of Computer Science and Technology, Jilin University, Changchun, Jilin Province, China
• ² Public Computer Education and Research Center, Jilin University, Changchun 130012, Changchun, China
• ³ Department of Radiology, First Affiliated Hospital of Jilin University, Changchun, Jilin Province, China

Multiple sclerosis (MS) and neuromyelitis optic spectrum disorder (NMOSD) are mimic autoimmune diseases of the central nervous system with a very high disability rate. Their clinical symptoms and imaging findings are similar, making it difficult to diagnose and differentiate. Existing research typically employs the T2-weighted fluid-attenuated inversion recovery(T2-FLAIR) MRI imaging technique to focus on a single task in MS and NMOSD lesion segmentation or disease classification, while ignoring the collaboration between the tasks. To make full use of the correlation between lesion segmentation and disease classification tasks of MS and NMOSD, so as to improve the accuracy and speed of the recognition and diagnosis of MS and NMOSD, a joint model is proposed in this study. The joint model primarily comprises three components: an information-sharing subnetwork, a lesion segmentation subnetwork, and a disease classification subnetwork. Among them, the information-sharing subnetwork adopts a dual-branch structure composed of a convolution module and a Swin Transformer module to extract local and global features, respectively. These features are then input into the lesion segmentation subnetwork and disease classification subnetwork to obtain results for both tasks simultaneously. In addition, to further enhance the mutual guidance between the tasks, this study proposes two information interaction methods: a lesion guidance module and a cross-task loss function. Furthermore, the lesion location maps provide interpretability for the diagnosis process of the deep learning model. The joint model achieved a Dice similarity coefficient (DSC) of 74.87% on the lesion segmentation task and accuracy (ACC) of 92.36% on the disease classification task, demonstrating its superior performance. By setting up ablation experiments, the effectiveness of information sharing and interaction between tasks is verified. The results show that the joint model can effectively improve the performance of the two tasks.