Multi-modal and multi-field fundus information fusion for retinopathy diagnosis via multi-scale cross-attention and shifted window self-attention

The joint interpretation of multi-modal and multi-field fundus images is critical for retinopathy prevention, as different fields can show the complete 3D eyeball field and different modalities can provide complementary lesion areas. Compared with single images, the sequence relationships in multi-modal and multi-field fundus images contain long-range dependencies in lesion features. By modeling the global contextual dependencies in these sequences, lesion areas can be more comprehensively mined, and modality-specific lesions can be detected. To learn the long-range contexts and fuse complementary multi-scale lesion features between different fundus modalities, we design a multi-modal fundus image fusion method based on multi-scale cross-attention, which solves the static receptive field problem in previous multi-modal medical fusion methods based on attention. To capture multi-field relative positional relationships between different fields and fuse comprehensive lesion features between different fields, we design a multi-field fundus image fusion method based on shifted window self-attention, which also solves the computational complexity of the multi-field fundus fusion method based on self-attention is quadratic to the size and number of multi-field fundus images. Finally, we design a multi-task retinopathy diagnosis framework to help ophthalmologists reduce workload and improve diagnostic accuracy by combining the proposed two fusion methods. The experimental results of retinopathy classification and report generation tasks indicate our method’s potential to improve the efficiency and reliability of retinopathy diagnosis in clinical practice, achieving a classification accuracy of 82.53% and a report generation BlEU-1 of 0.543.