CSANet: a lightweight channel and spatial attention neural network for grading diabetic retinopathy with optical coherence tomography angiography.

"the octa-dr data set comprised octa fundus images of 288 diabetic and 97 healthy individuals that were obtained using a swept-source oct system with a 12 mm x 12 mm single scan centered on the fovea (this data set is available at https://kyanbis github io/octadr )."

"the octa-dr data set comprised octa fundus images of 288 diabetic and 97 healthy individuals that were obtained using a swept-source oct system with a 12 mm x 12 mm single scan centered on the fovea (this data set is available at https://kyanbis github io/octadr )."

"Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://qims.amegroups.com/article/view/10.21037/qims-23-1270/coif). The authors have no conflicts of interest to declare."

"Funding: This work was supported by the Natural Science Foundation of Shandong Province ( No. ZR2020MF105 ), Guangdong Provincial Key Laboratory of Biomedical Optical Imaging Technology ( No. 2020B121201010 ), the Natural National Science Foundation of China ( Nos. 62175156 and 61675134 ), Science and Technology Innovation Project of Shanghai Science and Technology Commission ( Nos. 19441905800 and 22S31903000 ), and Qufu Normal University Foundation for High Level Research ( No. 116-607001 )."