M-DenseUNet: Multi Dense Encoder Connected UNet for Biomedical Image Segmentation
Tongdan Jin, Kaixu Chen, Satoshi Yamane, Yoshihiro Kuroda · 2022 IEEE 11th Global Conference on Consumer Electronics (GCCE) · 2022
In biomedical image segmentation, the desired performance is necessary for more detailed segmentation. In this paper, we propose the M-DenseUNet which combines multi Dense Encoders and U-Net. The encoder of M-DenseUNet consists of convolutional layers, Dense Block and Transition. We show that such a network can strengthen feature propagation and encourage feature reuse to get details.