Reading Diary Week 3
Reading Diary Week 3
5 Retinal Vessel Segmentation terms
[1] P. Yin, Y. Fang, and Q. Wan, “Dual attention multiscale network for vessel segmentation in
fundus photography,” Mathematics, vol. 10, no. 19, p.3687, 2022.
- Global features
- Which describes the visual content of the entire image by a single vector, this represent the texture, color, and shape information.
- Example: The encoder–decoder structure of UNet combines low-level local features with high-level global features to produce high-resolution prediction.
- Local features
- Refers to a pattern or distinct structure found in an image, such as point, edge, or small image patch.
- Example: The encoder–decoder structure of UNet combines low-level local features with high-level global features to produce high-resolution prediction.
- Multiscale networks
- This term is associated with feature extraction of an image by using convolutional blocks.
- Example: we introduce the popular attention mechanisms and multiscale networks for vessel segmentation.
- Receptive field
- The region of the input data which is provided to the neural network.
- Example: The deformable convolution block adaptively adjusts the receptive fields to capture vessels with variance shape and scale.
- Residual module
- A stack of layers set in such a way that the output of a layer is taken and added to another layer deeper in the block and mainly used to avoid vanishing gradient.
- Example: This research introduce residual modules to the generator for better representation learning ability.