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Found 11 papers, 4 papers with code
Super-Resolution Based Patch-Free 3D Image Segmentation with High-Frequency Guidance
The framework contains two sub-tasks, of which semantic segmentation is the main task and super resolution is an auxiliary task aiding in rebuilding the high frequency information from the LR input.
ScaleFormer: Revisiting the Transformer-based Backbones from a Scale-wise Perspective for Medical Image Segmentation
(2) A simple and effective spatial-aware inter-scale transformer is designed to interact among consensual regions in multiple scales, which can highlight the cross-scale dependency and resolve the complex scale variations.
Efficient and Accurate Hyperspectral Pansharpening Using 3D VolumeNet and 2.5D Texture Transfer
Different from the texture transfer processing of RGB image, we use HR PAN images as the reference images and perform texture transfer for each frequency band of MS images, which is named 2. 5D texture transfer.
Mixed Transformer U-Net For Medical Image Segmentation
Therefore, Vision Transformers have emerged as alternative segmentation structures recently, for their innate ability of capturing long-range correlations through Self-Attention (SA).
Genotype-Guided Radiomics Signatures for Recurrence Prediction of Non-Small-Cell Lung Cancer
The experiments demonstrated that the prediction accuracy can be improved significantly from 78. 61% (existing radiomics method) and 79. 14% (deep learning method) to 83. 28% by the proposed GGR.
Graph-based Pyramid Global Context Reasoning with a Saliency-aware Projection for COVID-19 Lung Infections Segmentation
Our Graph- PGCR module is plug-and-play, which can be integrated into any architecture to improve its performance.
PA-ResSeg: A Phase Attention Residual Network for Liver Tumor Segmentation from Multi-phase CT Images
In this paper, we propose a phase attention residual network (PA-ResSeg) to model multi-phase features for accurate liver tumor segmentation, in which a phase attention (PA) is newly proposed to additionally exploit the images of arterial (ART) phase to facilitate the segmentation of portal venous (PV) phase.
VolumeNet: A Lightweight Parallel Network for Super-Resolution of Medical Volumetric Data
We construct a parallel connection structure based on the group convolution and feature aggregation to build a 3D CNN that is as wide as possible with few parameters.
Unsupervised MRI Super-Resolution Using Deep External Learning and Guided Residual Dense Network with Multimodal Image Priors
Deep learning techniques have led to state-of-the-art image super resolution with natural images.
UNet 3+: A Full-Scale Connected UNet for Medical Image Segmentation
UNet, which is one of deep learning networks with an encoder-decoder architecture, is widely used in medical image segmentation.
Ranked #1 on
Medical Image Segmentation
on LiTS2017
A 2.5D Cascaded Convolutional Neural Network with Temporal Information for Automatic Mitotic Cell Detection in 4D Microscopic Images
In recent years, intravital skin imaging has been increasingly used in mammalian skin research to investigate cell behaviors.
