Abstract: Developing deep learning models for accurate segmentation of biomedical CT images is challenging ... we pioneer a deep quasi-recurrent self-attention structure that works with a dual encoder ...

With the recent advances in convolutional neural networks, vast improvements have been made for image segmentation, mainly based on the skip-connection-linked encoder-decoder deep architectures.

However, the manual label map for each training image contains more global and semantic ... In this paper, we have proposed a hierarchical encoder-decoder network for mitochondria segmentation from EM ...

The labels of each image were annotated by professional medical ... Convolutional neural networks (CNNs) are a common architecture for glioma segmentation, especially the encoder–decoder model. This ...

ABSTRACT: In recent years, deep learning has been widely used in the field of image understanding and made breakthroughs research progress in image understanding. Because remote sensing application ...

This repository contains the code of using Swithable Normalization (SN) in semantic image segmentation ... scripts/train.sh --arch_encoder architecture of encode network --arch_decoder architecture of ...