In order to study the effect of skip connections to segmentation performance in encoder and decoder networks, in this paper, we improve the skip connections of U-Net model and adopt the method of sub-module fusion connection. We fuse the high and low layers of the encoder by multi-head attention. Fusion is performed separately, and the fusion result is connected to the decoder. Considering that different input images have different effects to model training due to factors such as noise, we set the threshold by calculating the Euclidean distance between the image and the mask during training, so that different images use different skip connection methods. Experiments on Cell nuclei, Synapse, Heart, Chaos datasets show that FSC-UNet algorithm this paper proposed has better results than existing algorithms.
In order to improve the segmentation accuracy of polyp image segmentation under colonoscopy, we propose PVT Dual-Upsampling Net (PDNet). PDNet adopts the encoder network based on Transformer as the backbone network for downsampling, and designs a dual upsampling module based on cascaded fusion network and simple connection network to recover the loss of high-level image features caused by the downsampling process, and obtains a high-level semantic feature map with the same resolution as the input image. The multi-feature fusion module is used to aggregate the low-level feature map and high-level semantic feature map. We validate the model on three publicly available datasets, and our experimental evaluations show that the suggested architecture produces good segmentation results on datasets.
Access to the requested content is limited to institutions that have purchased or subscribe to SPIE eBooks.
You are receiving this notice because your organization may not have SPIE eBooks access.*
*Shibboleth/Open Athens users─please
sign in
to access your institution's subscriptions.
To obtain this item, you may purchase the complete book in print or electronic format on
SPIE.org.
INSTITUTIONAL Select your institution to access the SPIE Digital Library.
PERSONAL Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.