不改变原意使其更有逻辑改写：Using convolutional layers for retinal image feature extraction the features extracted from different convolutional layers are different and previous work has used fixed-resolution image

The use of convolutional layers for retinal image feature extraction results in varying features extracted from different layers. Previous research has only utilized fixed-resolution images with single-layer features to train the network model, failing to fully utilize the diverse feature information available. To address this, we improved the multi-scale representation capability of the backbone by implementing hierarchical residual-like connections within each single radix block of the original ResNest. This involved replacing the feature maps convolution layer with smaller groups of 3x3 filters and connecting them hierarchically in a residual-like style across different filter groups within each radix of ResNest