nmODE-Unet: A Novel Network for Semantic Segmentation of Medical Images
Shubin Wang, Yuanyuan Chen, Yi Zhang
Abstract
Diabetic retinopathy is a prevalent eye disease that poses a potential risk of blindness. Nevertheless, due to the small size of diabetic retinopathy lesions and the high interclass similarity in terms of location, color, and shape among different lesions, the segmentation task is highly challenging. To address these issues, we proposed a novel framework named nmODE-Unet, which is based on the nmODE (neural memory Ordinary Differential Equation) block and U-net backbone. In nmODE-Unet, the shallow features serve as input to the nmODE block, and the output of the nmODE block is fused with the corresponding deep features. Extensive experiments were conducted on the IDRiD dataset, e_ophtha dataset, and the LGG segmentation dataset, and the results demonstrate that, in comparison to other competing models, nmODE-Unet showcases a superior performance.